context and success in entrepreneurial decision making; an...
TRANSCRIPT
Context and Success in Entrepreneurial Decision Making; An
Empirical Validation
Erik van de Pieterman
University of Twente
Abstract
Effectuation theory claims to have superior predictive power over traditional ‘causal’ methods for understanding entrepreneurial decision making under conditions of uncertainty.
While gaining a lot of support initially, with some scholars talking about a possible paradigm
shift, recently there has been a call for more a more nuanced view. This thesis aims to
contribute to this debate by quantitatively testing the relationship of several context variables
with entrepreneurial decision making. Furthermore an attempt is made to construct a
multidimensional construct to measure entrepreneurial success in a way that is aligned with
effectual thinking. Data was collected from 87 expert and novice entrepreneurs by means of a
survey, and an additional existing dataset containing 185 respondents was included bringing
the total dataset to 272. Findings include evidence for the impact of cognition on decision
making, and a moderating effect of culture on that relationship. Interestingly, no direct or
moderating effect of expertise is found, even though this is a core argument for the
proponents of effectuation theory. Furthermore effectuation is found to be moderately
negatively correlated with causation, indicating that a dichotomous distinction between the
two styles is not productive. It is concluded that entrepreneurial decision making is a complex
process, dependent on subtle differences in contextual factors, but that effectuation might be
a fruitful avenue to challenge existing and traditional models of entrepreneurship.
Keywords: Effectuation, causation, expertise, culture, cognition, entrepreneurial succes, decision making.
Supervisory committee:
First supervisor: dr. M.R. Stienstra
Second supervisor: dr. M.L. Ehrenhard
Ad interim supervisor: dr. R.P.A Loohuis
Faculty of Behavioural Management and Social Sciences
University of Twente
Date: 25-07-2018
Contents
Chapter 1. Introduction .............................................................................................................. 2
1.1 The origins of the field ..................................................................................................... 2
1.2 The need for context ......................................................................................................... 3
1.3 Planned versus emerging theories .................................................................................... 3
1.4 Purpose and outline of the thesis ...................................................................................... 5
1.4 Scientific & societal relevance ......................................................................................... 6
Chapter 2. Theoretical framework ............................................................................................. 7
2.1 Introduction ...................................................................................................................... 7
2.2 Effectuation theory ........................................................................................................... 7
2.3 On context ...................................................................................................................... 10
2.4 Cognition - Entrepreneurship as a mode of thinking ..................................................... 12
2.5 Culture - Local contingencies influencing behavioral preferences ................................ 15
2.6 Expertise - What makes an expert? ................................................................................ 17
2.7 Entrepreneurial outcomes - A holistic notion ................................................................ 19
Chapter 3. Model & Hypotheses .............................................................................................. 24
Chapter 4. Methodology .......................................................................................................... 26
4.1 Introduction .................................................................................................................... 26
4.2 Research Design ............................................................................................................. 26
4.3 Sample ............................................................................................................................ 27
4.4 Operationalization & instruments .................................................................................. 28
4.5 Procedures ...................................................................................................................... 33
4.6 Validity ........................................................................................................................... 33
4.7 Ethical considerations .................................................................................................... 34
4.8 Conclusion on methods .................................................................................................. 35
Chapter 5. Results .................................................................................................................... 36
Chapter 6. Conclusion .............................................................................................................. 38
6.1 Interpretation of the results ............................................................................................ 38
6.2 Conclusion ...................................................................................................................... 40
Chapter 7. Discussion .............................................................................................................. 42
8. Bibliography ........................................................................................................................ 42
9. Appendix .............................................................................................................................. 44
Chapter 1. Introduction
“The answers you get depend on the questions you ask.”
~Thomas S. Kuhn
1.1 The origins of the fi eld
Entrepreneurship is probably as old as mankind itself (Tatyana & Aleksandra, 2013). The
first known trading occurred already around the year 17,000 BC. Small communities of
hunters and gatherers would venture out to other tribes in search of a place where they could
exchange their surplus goods in return for items that they themselves did not have. The
agricultural revolution, some 12,000 years ago, made it possible for people to increasingly
specialize in their labor. The introduction of debt and currency made the exchange of goods
significantly easier. The scientific and the subsequent industrial revolution marked the
beginning global capitalism (Cook, 2004).
The early twentieth century saw the beginnings of our current conception of the social
sciences, and the idea that society may be studied in a standardized and objective manner.
The idea was to apply mathematics and the search for universal laws from the natural
sciences to the realm of society. How wonderful would it be to be able to predict and control
parts of society in the same way as we are able to with nature. As such our modern social
sciences were born. In the field of economics and business, Schumpeter (1934) pioneered in
systematically reflecting on the nature of entrepreneurship. He defined entrepreneurship not
only in terms of economic gains, but also as a drive of innovation and technological change.
Entrepreneurs not only contribute to a nation’s wealth, but are also capable of being the mediators between newly developed scientific knowledge and its application in our society.
So entrepreneurship as an academic discipline was born. As with many social sciences which
are based on human action, a clear definition is difficult. Most people know intuitively what
is meant by entrepreneurship, but a clear definition of the concept in academia has been
subject to a lot of discussion (Carton et al., 1998). Contributing to this ambiguity is the fact
that the subject can be approached from many different angles and disciplines, such as
sociology, economics, psychology, and management (Hebert & Link, 2009). It can be
described as a multifaceted phenomenon (Low & MacMillan, 1988). Some even argue that
we should not try to reach agreement on an exact definition of entrepreneurship (Stearns &
Hills, 1996). Such is the fate of applying the scientific method to a social phenomenon as
elusive as entrepreneurship.
However, good attempts to formalize and mathematize the field of entrepreneurship were
undertaken in a quest to predict and control aspects of entrepreneurship. This led for example
to a search for personality indicators of successful entrepreneurs (Zhao & Seibert, 2006), the
ability of entrepreneurs to predict the future and control uncertainty (Langlois & Cosgel,
1993), or a formal description of factors leading to entrepreneurial success (Roure &
Maidique, 1986). Ideally, a kind of formulae would be established, where different factors of
entrepreneurship could be filled in and which would result in a predetermined optimal course
of action. However, it turned out that humans do not always behave rationally (Tversky &
Kahneman, 1973), and are therefore difficult to predict or capture in theories and laws.
Moreover, postmodernism eroded our notion of universal scientific truth and opened up the
way to see things from many different, but equally important perspectives. Add to this a
globalizing world in which fast changing circumstances lead to an ever increasing
complexity, and it will become apparent that a scientific methodology based on the natural
sciences might not yield the expected results when confronted with something illusive as
entrepreneurial behavior.
1.2 The need for context
The study of entrepreneurship develops perhaps along similar lines as our understanding of
and knowledge about the world does. While postmodernism is sensitive to local
circumstances and mundane situations, rejecting notions about the universality of ideas and
ideologies (Hassan, 2001), in recent entrepreneurship research there is a similar call for
attention to context (Brinckmann et al. 2010; Welter, 2011; Zahra, 2007). Contextualizing
entrepreneurship research means seeing entrepreneurial activity as being determined by
historical, ecological and social contingencies, rather than flashes of an individual
enterprising genius, or following a fixed method for starting new businesses. Recognizing the
need for differences in context we can see that measuring success in entrepreneurship
research is often simplistic, and in need of including aspects other than merely financial
performance indicators (Amit et al., 2000; Cohen & Mitchell, 2008; Davidsson 2016; Fisher
et al., 2014; Sarasvathy, Menon & Kuechle, 2013).
As for the field of entrepreneurship as an academic field of study, it can traditionally be
defined as being concerned with the study of sources of opportunities; the processes of
discovery, evaluation, and exploitation of opportunities; and the set of individuals who
discover, evaluate, and exploit them (Venkataraman, 1997; Shane & Venkataraman, 2000).
The role of the entrepreneur is thereby seen as discovering and the subsequent exploitation of
new venture opportunities (Carton et al. 1998). However in the last two decades, an
alternative view has emerged, one that examines how certain types of entrepreneurial action
can result in forming rather than merely finding or encountering opportunities (Welter et al.,
2016).
1.3 Planned versus emerging theories
The above mentioned changing views are recognized by scholars in the field of
entrepreneurship, and has led to the development of a stream of research that is called
emergent, as opposed to planned theories of entrepreneurship research (Fisher, 2012).
Two prominent examples of such emergent theories are bricolage and effectuation, which
both investigate the nature of entrepreneurial decision making.
Bricolage is a concept with an history dating back to the French anthropologist Lévi Strauss
(1966), who used it as a way of describing an epistemology based on indigenous
mythological thought. The novelty and attractiveness of the concept came from the fact that it
described a non Western way of thinking which nevertheless seemed productive and useful.
Since then the concept has been applied in various fields of research ranging from biology
(Lewens, 2013), to educational science (Wagner, 1990) and being used in theater, arts and
music (Dijk, 2012).
In the field of business administration bricolage as a managerial tool is often used to describe
a rational response to environmental constraints, mostly being resource scarcity and
unexpected events. As such, it can be used as an approach or tool to aid in solving problems,
designing, and innovating in uncertain contexts (Visscher et al. 2017). Not surprisingly it has
therefore also been picked up by researchers of entrepreneurial behavior (Phillips & Tracey,
2007) as a way of supplementing traditional theories on entrepreneurial behavior (Fisher,
2012). In this context, Baker & Nelson (2005) describe bricolage as ‘’making do with whatever resources are at hand, combining of resources for new purposes, and the refusal to
enact limitations.’’
Effectuation theory was first developed in the seminal article by Sarasvathy (2001). She starts
from the premise that traditional thinking in economics and management is flawed by
assuming the existence of artifacts such as organizations and markets. They are not
adequately equipped to explain how such artifacts come into being, and therefore fail to
explain a crucial component of how new businesses are developed. She argues that in all the
renowned places where business is being taught, the methods rely on assumptions that
multiple factors of a business environment are known and using forecasting and estimating
techniques, answers to unknown variables can be extrapolated (Sarasvathy, 2008). In practice
however, many decisions are subjected to Knightian uncertainty, which is defined as risk that
is immeasurable and not possible to calculate, or not only unknown but also unknowable
(Perry et al. 2012; Sarasvathy et al., 2003). Effectuation theory is able to bridge this gap, and
provide an explanation of how new firms are created and decisions are made under conditions
of such uncertainty (Dew et al. 2008; Read & Sarasvathy, 2005; Wiltbank et al. 2006).
Research has shown that traditional models of entrepreneurial decision making, such as those
commonly taught in business schools, may not effectively capture and reflect the actual behavior of entrepreneurs launching new ventures in a dynamic environment (Fisher, 2012).
This creates the need to at least supplement these traditional causal models with those that
have an emergent perspective on decision making. As perhaps already became clear from the
description, bricolage and effectuation appear to be similar in many respects as they provide
a basis for the analysis of relations between resources, entrepreneurial opportunities, resource
constraints and creativity (Archer et al. 2009; Fisher, 2012).
Because of the similarities, a choice for effectuation is made as the prime theory for this
thesis. The reason for this is that effectuation yields more results when searching the
academic databases, perhaps because it is viewed by scholars as more promising and exciting
since some already talk about a paradigmatic shift in our thinking about entrepreneurial
decision making (Perry et al., 2011). While talking about things like a changing paradigm
should not be done lightly because it requires radical change in the way we conceive of basic
concepts and methods in a field of science (Kuhn & Hawkings, 1963), effectuation does seem
to fundamentally question the way we think about entrepreneurship. Even though a lot of
work still needs to be done if effectuation is to qualify as a real theory (Arend et al., 2015), it
does hold much promise for better understanding the complex ways in which entrepreneurs
make decisions.
1.4 Purpose and outline of the thesis
In light of the picture sketched above, the aim of this thesis is to explore why and especially
in what context we should use effectuation theory to understand entrepreneurial decision
making. Contributing to a nuanced understanding on the range of possibilities and situations
that allow effectuation theory to be used will hopefully result in more effective and
productive use of the theory. As an additional component, a critical assessment is made on
the traditional use of entrepreneurial success, and a new measurement is introduced which
should do justice to a more emergent view of entrepreneurship.
In order to do so, a theoretical framework is built by drawing several concepts together in
order to understand the background in which entrepreneurial decision making takes place. In
the framework. the concept of context and its role in academic research on entrepreneurship
is explored (Boettke & Coyne, 2009; Brinckmann et al. 2010; Welter, 2011; Zahra, 2007).
Three context variables are found in the literature that are expected to significantly influence
entrepreneurial behavior.
First, looking at entrepreneurship from the perspective of expertise can be considered as key
to effectuation theory (Sarasvathy, 2009), and a main argument in support for effectuation
theory is that it turned out that entrepreneurs which are classified as experts would choose an
effectual approach more often than novices would (Dew et al. 2009). Secondly, seeing
entrepreneurship primarily as a mode of thinking opens up the way for an underlying
cognitive order in entrepreneurial behavior, also called a global culture of entrepreneurship
(Mitchell et al., 2000;2002). The third context variable found in the literature is considered
less important by Sarasvathy (2009) but might nevertheless play a significant role (Triandis,
2004) and represents the concept of culture.
After the analysis of the context and its relevant factors, the theoretical framework ends with
a reflection on currently used notions for measuring performance. After concluding that
contemporary measures are not adequate to facilitate a changing view on the nature of
entrepreneurship, a new construct is developed. The result is a holistic conception of
entrepreneurial success grounded in multiple levels of analysis. An attempt is made to go
beyond only financial performance indicators in order to align the concept of success with the
effectual school of thought and what it means to be an entrepreneur.
Based on the theory, a conceptual model is developed in order to empirically test the
corresponding hypotheses on the importance of context for effectuation theory. Data on one
hundred expert entrepreneurs is collected to determine the effects of context on
entrepreneurial behaviour and if a causal relationship can be found between the type of
entrepreneurial decision making and success. The results are then presented followed by a
discussion on their implications in the conclusion.
Based on the above, a research question is formulated:
How do thinking style, culture, and expertise influence the entrepreneurial decision making
process, and what is the predictive value for entrepreneurial success?
1.4 Scientific & societal relevance
Effectuation is a promising theory but still under development and more empirical validation
is needed to strengthen it (Perry et al., 2011). A common method to test the decision making
process is to use think aloud protocols (Dew et al., 2008; Sarasvathy, 2009). While think
aloud protocols are suitable for explorative research, a survey offers a more quantitative
approach to testing the robustness of the theory (Babbie, 2013). The scientific relevance of
this thesis therefore lies in contributing to the empirical validation via quantitative means of
effectuation theory. This will in the end lead to a better understanding of the forces that
influence entrepreneurial decision making. Furthermore the issue of measuring performance
or success, something which remains a challenging aspect in research on entrepreneurship
(Fisher, 2012; Murphy, Trailer & Hill 1996), is addressed by developing a construct that is
hopefully better suited to measure entrepreneurial outcomes from an effectuation perspective.
The social relevance of this thesis stems from the strong connection of entrepreneurship
research to real world practice. The underlying goal of academic understanding of
entrepreneurship is, or ought to be, to aid in better entrepreneurial related decision making.
This is beneficial for our society for several reasons. Entrepreneurial activity is empirically
proven to influence factors such as employment and job creation (Block et al., 2017),
productivity growth and innovation (Praag en Versloot, 2007). Following the perspective
proposed by Sarasvathy (2001), one could argue that next to beneficial outcomes for society,
entrepreneurship is also a way to cultivate virtues (Sand, 2017). It is a possible pathway to
attain personal autonomy in one’s life, and according to Aristotle it can therefore ultimately
contribute to the individual eudaimonia or good life (Barnes, 1984). Contributing to the
development of effectuation theory and its outcomes is therefore directly relevant for
understanding entrepreneurial decision making, whether this understanding is used by
academics, policymakers, or entrepreneurs themselves.
Chapter 2. Theoretical framework
“Science may be described as the art of systematic over-simplification.”
~Karl Popper
2.1 Introduction
The aim of this chapter is to develop a framework of contextual factors affecting
entrepreneurial behavior, and to explore avenues for a more suitable measure of
entrepreneurial success. First effectuation theory is explored. Following that, an argument for
the inclusion of context and the relevant contextual factors is presented. A theoretical
description of the relevant contextual factors is then given. In the final section the idea of
entrepreneurial success is examined.
2.2 Effectuation theory – What makes a decision
2.2.1 Introduction
As explained in the first chapter, a combination of developments has led to the rise of an
emergent stream of research in the field of entrepreneurship. Opposed to a planned approach,
such a way of thinking might be more effective to explain contemporary issues relating to
entrepreneurial processes. One of the promising theories in this regard is that of effectuation,
first described by Sarasvathy (2001). This section contains an overview of the most important
ideas in effectuation theory.
Effectuation theory was first developed in the seminal article by Sarasvathy (2001). This
theory describes the entrepreneurial decision making process. Sarasvathy also recognizes that
traditional planned approaches to describe entrepreneurship are not optimal. She shares these
approaches based on a logic of prediction under the concept of causation. When
entrepreneurs develop new products and enter new markets, the environment is too complex
to rely on prediction. Instead relying on a logic of control is more suitable according to
Sarasvathy. In her seminal article she articulates this theory (Sarasvathy, 2001), but also
sketches a different picture of the entrepreneurial nature. This is perhaps a conceptualization
which is better suited to understanding entrepreneurship in our contemporary society. About
an entrepreneur who is not only strategically looking for windows of opportunities to gain
optimal return on investment, but rather as a person with a unique set of capabilities and
resources based on historical determined contingencies. Someone for who building a business
is also a way to achieve life goals, and who might not have a clear vision of the road ahead
for the venture but experiments and makes use of the situation as it unfolds.
2.2.2 Effectuation explained
Effectuation begins with means available to the effectuators. Three categories can be
distinguished based on three personal aspects of the entrepreneur. ‘Who I am’ refers to the traits, abilities and attributes of the entrepreneur. ‘What I know’ relates to the level of
education, experience and expertise. ‘Whom I know’ is comprised by the entrepreneur's social networks. These three personal circumstances together form the ‘what I have’, the pool of resources available to the entrepreneur (Sarasvathy et al., 2008; Sarasvathy, 2008). The
central question then becomes: what effects, such as the creation of new firms or markets, can
I create, given who I am, what I know, and whom I know? This leads to several possible
courses of action of which the consequences are to a large extent unpredictable, and typically
co-determined by interested stakeholders. For effectuation theory, the focus of the entire
decision-making process for each individual involved is on what can be done with the set of
given means or resources (figure 2.1).
Figure 2.1: A dynamic model of effectuation, based on Sarasvathy (2008).
Effectuation is often contrasted to the traditional, causal view of entrepreneurship (Perry et
al., 2011; Sarasvathy, 2003; 2008; Wiltbank et al., 2009). Sarasvathy (2001, p. 245) states
that effectual processes “take a set of means as given and focus on selecting between possible
effects that can be created with that set of means”, while causation processes “take a particular effect as given and focus on selecting between means to create that effect.” Contrasting these two different approaches can give insight into what defines effectuation.
Dew, Read, Sarasvathy, and Wiltbank (2009) provided an overview of the characteristics of
effectuation as opposed to causation. These will be briefly elaborated below.
Non-predictive as opposed to predictive control
The logic of control differs to the extent that an effectual logic will rely on measures of
controllable aspects of an uncertain future. This is opposed to a causal logic in which a
decision maker chooses between alternatives based on predictions about favourable
outcomes. Using an effectual logic it would make sense to establish relations or contracts
with a customer in order to develop a product which satisfies the conditions that are set. An
causal logic would be to perform market analysis and research in order to develop a product
to suit consumer needs, and then selling it to the customer.
Means-driven as opposed to goal-driven action
Effectuation is characterized by a means-driven action, while causation is oriented towards
goal-driven action. This distinction is related to the other emergent theory of
entrepreneurship, that of bricolage, which can be described as ‘’making do with whatever resources are at hand’’ (Baker & Nelson, 2005). An entrepreneur using an effectual logic is best off taking action based on what is readily available: who you are, what you know, and
who you know. The available resources are partly determining the goals. In contrast, an
entrepreneur using a causal logic will first set a desired goal, and subsequently will try to
gather the resources necessary in achieving such outcome.
Affordable loss as opposed to expected return
From an causal point of view, the best course of action would be the one that will maximize
future expected returns. When having to choose between different sets of actions, a
calculation reveals which action or project will yield the highest expected returns. From an
effectual perspective, the choice of action or project will depend more upon the assessment of
what the entrepreneur is able or willing to lose. If potential losses are within the bounds of
acceptability, the decision will per definition not become unacceptable in terms of resources
spent.
Partnerships as opposed to competitive analysis
An important aspect of the effectual logic is to rely on partnerships with relevant
stakeholders. Forming alliances and bringing such stakeholders on board are important, even
in the stages where the exact product-market combinations are still unclear. The forming of
alliances and the input of stakeholders can influence the direction of the new firm.
Conversely, and causal logic will rely more on competitive analysis and strategic planning to
define markets, customers and segments. Once these variables are known, the relevant
stakeholders are identified and acquired.
Leveraging as opposed to avoiding contingencies
A combination of the above mentioned characteristics of effectuation such as getting major
stakeholders on board early, working from one’s available means and having acceptable levels of downside risk, means that contingencies are less likely to be harmful. Not having a
fixed point of destination in the future means that contingencies might alter the goal of the
newly founded firm, but that this is not necessarily problematic, and that contingencies can be
leveraged. This is opposed to a causal approach, where a clear goal is defined and
contingencies are seen as unexpected events that can only distract from the goal and should
therefore be avoided. Effectual entrepreneurs are able to see uncertainty and unexpected
events as a resource and use them to their advantage.
Figure 2.2: Causal versus effectual logic, based on Dew et al. (2009).
2.2.3 Conclusion
To sum up, effectual decision making relies on a set of resources available to the
entrepreneur, and following a course of action into the unknown, preferably with some key
stakeholders as a companion. This is opposed to a causal type of decision making, where the
entrepreneur strives towards a predetermined goal, making use of market analysis to outsmart
competitors. Causal decision making is thus based on prediction, while effectuation acts upon
a logic of controlling certain aspects that can influence the future. Under conditions of
Knightian uncertainty, the effectual approach to decision making can be considered to better
explain how new entrepreneurial artifacts such as firms and markets come into being. The
next question is why entrepreneurs choose either a casual or effectual way of making
decisions. This may in part depend on the context, which will be explored in the next section.
2.3 On context
Academic research on entrepreneurship has long been attentive to the fact that organizations
do not operate in isolation. Rather they are a player in what is called the business
environment, where external factors such as customers, competitors, governments, and
markets play an important role (Kaplan & Norton, 2001). Effectuation theory starts from the
premise that the entrepreneurial process is in part shaped by contextual factors such as the
entrepreneur’s personal characteristics, level of education and expertise, and available resources such as social networks or financial means (Sarasvathy, 2001).
However, it is still a question why some entrepreneurs make more use of an effectual
approach in their decision making, while others prefer a more planned or causal approach.
Proponents of effectuation theory claim that entrepreneurs who are considered to be experts,
are more likely to choose the effectual approach (Dew et al., 2009; Read & Sarasvathy,
2005), suggesting that effectuation is in fact better suited for entrepreneurial decision
making. Others take a more nuanced approach, arguing that the effectiveness of planned and
emergent approaches are dependent on the context in which they are used (Brinckmann et al.
2010).
Being sensitive to contextual factors in entrepreneurship research will increase its quality
(Zahra, 2007). In order to gain a better understanding of the reasons why entrepreneurs
choose a specific decision making process, several contextual factors that might have a
moderating or mediating influence on this relation will be explored in later sections of the
theoretical framework. The rest of this section is devoted to a brief description on
entrepreneurial context beyond the business environment.
One of the key concepts to understanding context is that of institutions. Boettke & Coyne
(2009) see institutions as making up the formal and informal “rules of the game.” These rules create payoffs that make certain entrepreneurial opportunities more attractive than others.
Institutions can be either formal or informal (Peng, 2003), and can be seen as structures and
mechanisms of social order. Formal institutions are often seen as stemming from official
organisations, such as the government, to provide rules in the form of legislation and
regulation, property rights and the judicial system (Li & Zahra, 2012). Informal institutions
can be seen as the soft rules relating to human psychology, such as social norms, culture and
family (Helmke & Levitsky, 2004).
Institutions form the rules of the game, and collectively these rules make up the context in
which the entrepreneur operates (Boettke & Coyne, 2009). Calling for the contextualization
of entrepreneurship, Welter (2011) develops four dimensions of context. She argues that
distinguishing between business, social, spatial, and institutional context, leads to better
understanding of how external factors influence entrepreneurship on multiple levels of
analysis. Opening up the discussion on the diversity of contexts of entrepreneurship will
therefore be a step towards understanding the nature and dynamics of entrepreneurship
(Zahra, 2007).
For the purpose of this thesis, three context variables are chosen that seem to have a
prominent place in the literature. First, Welter (2011) sees that an important part of the
institutional context consists of culture. The importance of culture as a contextual factor in
entrepreneurship is supported by other authors (Brinckmann et al. 2010; Hayton et al., 2002;
Meek et al., 2010). The second contextual factor stems from a psychological stream of
thought and constitutes the influence of cognitive aspects on entrepreneurial behaviour
(Baron, 1998; Mitchell et al., 2000; 2002). This line of reasoning is seen as an interesting
avenue for research into driving forces of entrepreneurial behavior (Laskovaia, 2017). The
third context variable, and relating to the work on entrepreneurial cognitions is the premise
that one of the key factors determining the choice for an effectual approach is the level of
entrepreneurial expertise (Read & Sarasvathy, 2005). While evidence is already found for this
relation by Dew et al. (2009), methodological issues in their article and the possibility for a
more nuanced relation justify more extensive empirical validation on this matter.
To conclude, the context in which entrepreneurial activity takes place can help to better
understand the entrepreneurial process and decision making procedures. Based on the
literature, three major contextual factors, cognition, culture, and expertise are chosen and
explored further on in the theoretical framework. These factors forms the basis for the
empirical investigation on the antecedents of entrepreneurial decision making, and being
attentive to these context factors should ideally help us gain a better understanding of its
workings.
2.4 Cognition - Entrepreneurship as a mode of thinking
2.4.1 Introduction
Recent years have seen considerable interest in cognitive logics used by entrepreneurs
(Laskiovia et al. 2017). Research into cognition has established that different thinking styles
are better suited for different tasks or activities, and that the nature of the task influences the
degree to which a specific thinking style is adopted (Novak & Hoffman, 2008). In this section
the influence of cognitive thinking styles on the choice for either an effectual or causal logic
by the entrepreneur is explored. To do this, the cognitive model of Mitchell (2000) is
reviewed to see how cognition and the concepts of cognitive scripts are related to
entrepreneurship. These cognitive scripts are then translated to Epstein’s (1996) conception of intuitive-experiental and analytical-rational thinking styles.
2.4.2 Entrepreneurial cognitions
Mitchell et al. (2000) start from the premise that entrepreneurship is primarily a mode of
thinking, meaning that differences in individual cognitive aspects are responsible for
entrepreneurial behaviour. What they call entrepreneurial cognitions can explain important
phenomena in global entrepreneurship. For them it is conventional wisdom that the factors
influencing the decision to start a new business are different across countries. However, this
apparent multitude of diverse phenomena might not be so different after all. Even though
entrepreneurs are located in different cultures, they also share some important ways of
thinking, and taken together these form the elements of a coherent cognitive model. As such,
several heterogeneous factors that influence the decision to start a new business are in fact
subject to an underlying cognitive order (Mitchell et al., 2000;2002).
Cognition is defined as ‘’all processes by which sensory input is transformed, reduced,
elaborated, stored, recovered and used’’ (Neisser, 1967 in Mitchell et al. 2002). This definition is combined with the use of expert information processing theory, since
expertise can account for the ability of entrepreneurs to transform, store, recover and use
information that nonentrepreneurs do not have. As such a distinction between those two
groups can be made. Experts are in the possession of knowledge structures, also called
scripts, that allow them to outperform nonexperts who do not possess such scripts. As such,
their conceptual model (figure 2.3) is based on the influence of cultural values on cognitive
scripts, which in turn determine the venture creation decision.
Figure 2.3: Conceptual model of culture and cognition based on Mitchell et al. (2000).
In this model, cultural values are defined as the way that human societies organize knowledge
and social behaviour into a consistent set of cognitive orientations. In their study, the authors
use two of the most used cultural values as described by Hofstede (1980), namely
individualism and power distance. These values influence the respective cognitive scripts.
Arrangement scripts are related to the individuals propensity to having contacts,
relationships, resources and assets necessary to form a new venture. These scripts are the
knowledge structures of individuals about the use of these arrangements to support their own
performance.
Willingness scripts are the knowledge structures that are related to the commitment of
receptivity of starting a new venture, and form the basis of thoughts that can inspire action
towards the creation of a venture. Finally, ability scripts are the cognitive structures that
individuals have about capabilities, skills, knowledge, norms and attitudes that are needed to
create a new venture. In a dynamic interplay with each other, these scripts, moderated by
cultural values, can determine if a person will choose to start a new business.
In sum, Mitchell et al. (2000;20002) argue that shared cultural characteristics within a group
of people can lead to this group being faced with similar problems and related responses,
which in turn leads to a kind of shared cognition based on these shared experiences. It can
furthermore be argued that entrepreneurs, regardless of cultural or spatial differences, also
share this experience of similar problems and responses, related to the inherent nature of
entrepreneurship. This in turn leads to a shared entrepreneurial cognition, or what Mitchel et
al., (2000:2002) call a global culture of entrepreneurship.
2.4.3 Cognitive-experiential self theory
From the framework of Mitchell et al. (2002) it becomes clear that culture and related
cognitions can influence entrepreneurial decision making, with the new venture creation
decision in particular. However to explore the influence of cognition on the use of either a
effectual or causal method, the cognitive-experiential self theory (CEST) developed by
Epstein (1994) is deemed more appropriate. This is first because of methodological issues,
for example the inability to directly measure the degree of mastery of cognitive scripts since
they are, as mental operations, not directly observable (Mitchel et al., 2000; p.982).
Furthermore CEST makes use of a dichotomous distinction between two types of information
processing modes. As will become clear, these two modes correspond well to the two types
of entrepreneurial processes under scrutiny.
Cognitive-experiential self theory (CEST) was developed by Epstein (1994), under the
assumption about the existence of two parallel, interacting modes of information processing.
Based on many conceptualizations about differences in information processing by a variety of
high-ranking psychologists such as Jung, Tversky and Kahneman, and Pavlov, Epstein
inferred the existence of a rational and an experimental mode of information processing.
The rational system operates primarily at the conscious level and is intentional, analytic, and
mostly verbal and relatively free of emotional affect. The experiential system on the other
hand is more automatic driven, it operates below the surface of consciousness, is more
nonverbal and is intimately associated with emotional affect. The table below summarizes the
main differences between the rational and experiential system.
Figure 2.4 Experiential vs Rational Systems, based on Epstein et al. (1996).
These two modes of information processing share important characteristics with the two
entrepreneurial decision models of effectuation and causation. An effectual process looks
similar to the experiential or intuitive system while the causal approach seems to share a lot
of features with the rational system.
In their 1996 study, Epstein et al. furthermore explore the possibility of individual differences
in the usage of these two thinking styles, a study in which they measure to which extent
people characteristically operate in either of these modes. Evidence for individual differences
is found along the conclusion that the two modes of processing are not antagonistic but are
two kinds of information processing that are independent, something which is also expected
to be found in effectuation theory when comparing the effectual and causal logic. Although
rational and experiential are independent, they jointly contribute to behavior. This means that
there are individual differences in thinking style, and that these lead to differences in action.
Because both the apparent similarities between the two thinking styles and entrepreneurial
processes, and methodological issues in measuring scripts, CEST will be included in the final
theoretical framework in favor of cognitive scripts.
2.5 Culture - Local contingencies influencing behavioral preferences
2.5.1 Culture in Entrepreneurship
Culture is seen as an important contextual factor in entrepreneurship (Brinckmann et al.
2010). People in collectivist cultures place more emphasis on context rather than content.
This is important for entrepreneurial decision making because collectivists see behavior as a
result of external factors such as norms and roles (Triandis, 2004). Furthermore, culture is a
shaping force in cognitive scripts and subsequent entrepreneurial cognitions and decision
making. As can be seen in figure 2.3, the conceptual model as proposed by Mitchell et al.
(2000), culture both influences the cognitive scripts directly, and has a moderating effect on
the relation between cognitive scripts and entrepreneurial decision making.
A common conception of culture is that which has been proposed by Hofstede (1980) as: the
collective programming of the mind, which distinguishes the members of one group of people
from another.” Hofstede’s cultural dimensions are widely known as a conceptual tool for cross-cultural business analysis, however his work is not without drawbacks (Tung &
Verbeke, 2010). Although the use of values to understand cultural differences has dominated
the field, there is growing recognition that new perspectives are needed to supplement this
approach (Gelfand, 2006). Able to draw relations from societal cultural influences to
individual behavior and organisational context, the cultural-tightness concept can prove
fruitful to explore entrepreneurial processes (Gelfand et al., 2006; 2011; Uz, 2015).
2.5.2 Cultural tightness-looseness
Scholars from anthropology and psychology have long argued that the strength of social
norms and sanctioning is an important component of the societal normative context.
Therefore Gelfand et al. (2006) developed cultural dimensions on the strength of societal
norms and sanctions that link external societal constraints with individuals’ psychological processes and organizational processes.This led to the distinction between tight and loose
culture. Tight cultures are defined as having strong norms and a low tolerance of deviant
behavior. Loose cultures have weak norms and a high tolerance for deviant behavior (Gelfand
et al., 2011). Variation in this distinction of norms and sanctions can be used to see how
cultural factors can social and moral attitudes across the globe (Mrazek et al., 2013).
The degree of cultural tightness, being the strength of norms and degree of tolerance for
deviance, is determined by several distal ecological and human made threats. In response to
those threats, social institutions and practices are developed which together shape the
tightness of a culture (Gelfand et al., 2011; Uz, 2015). A high occurrence of threats, be it
natural or man-made, would increase the need for strong social norms and punishment of
deviant behavior. This allows better social coordination, and a higher chance of survival. It
is important, Gelfand et al. (2011) state, to refrain from value judgements on the desirability
of a tight or loose culture from one’s own vantagepoint. This because the these cultural
aspects are in part functional in their own ecological and historical context. Figure (2.5)
shows how a combination of institutional, ecological and historical factors coupled with more
mundane situational aspects affect the development of societal norms and tolerance of
deviant behavior.
Figure 2.5: A systems model of tightness-looseness, based on Gelfand et al. (2011).
The degree of cultural tightness is thus determined at a collective level, and its influence
trickles down to an organisational and individual level (Gelfand, 2006). The strength of
norms and sanctioning influence a variety of aspects ranging from organizational context and
outcomes, to individual behavior and decision making styles. This way it becomes clear that
the historical and ecological circumstances which are shared by a collective of people, can
influence everyday entrepreneurial decisions by being incorporated into their culture. Both
from the behavioral perspective of individuals and through the institutional context in
organizations. It can therefore be expected that the degree of cultural tightness affects the use
of effectual decision making, and perhaps also the final result, firm success.
2.6 Expertise - What makes an expert?
2.6.1 Introduction
Looking at entrepreneurship from the perspective of expertise can be considered as key to
effectuation theory (Sarasvathy, 2009). Instead of looking at traits or circumstances for trying
to explain variance in performance, effectuation theory looks into understanding
commonalities across a variety of of high performers, or experts, in a given domain. A main
argument in support for effectuation theory is that it turned out that entrepreneurs which are
classified as experts would choose an effectual approach more often than novices would
(Dew et al. 2009). If experts in the field of entrepreneurship choose an effectual approach
more often, surely this would be a sign of the effectual approach being more effective than
using causal methods. Therefore the subsequent section will look into how expertise in the
context of entrepreneurship looks like, by what ways it can be acquired, and how experts
distinguish themselves from novices in terms of results.
2.6.2 Expertise in entrepreneurship
There is little argument that expertise is contextual (Dew et al., 2009), meaning that it is
dependent on situational aspects. Expertise therefore has to be examined in separate domains,
and it can be defined as attaining reliable superior performance in such a particular domain.
Experts are able to do this because they possess knowledge structures, or scripts, about
particular domains that allow them to significantly outperform non-experts who do not have
and use such structured knowledge (Mitchell et al. 2000). Seeing entrepreneurship as a form
of expertise is in line with recent productive developments in cognitive psychology on the use
of heuristics (Read & Sarasvathy, 2005).
In order to be successful entrepreneurs have to have diverse skillsets, including “knowing the business and markets, being an entrepreneurial force, accommodating adversity, as well as
oral presentation skills, interpersonal skills, and the ability to prepare and present a business
plan’’ (Gustafsson, 2006, p.44). However in order to distinguish an expert entrepreneur from a novice, she has to to be able to perform the whole range of tasks at different levels of
uncertainty. Something which requires the ability to match decision-making mode to the
nature of the task (Mitchell et al. 2005). This is what entrepreneurial expertise really entails
according to Gustafsson (2006), and entrepreneurs are able to match task and decision frame
by the use of entrepreneurial scripts as described in section 2.3. These scripts should be
derived from both successes and failures in situations characterised by different levels of
uncertainty in order to let the entrepreneur become a true expert capable of making adequate
decisions across a variety of tasks.
Dew et al. (2009) empirically studies the differences between expert entrepreneurs and MBA
student they considered to be novice entrepreneurs trained in formal academic methods. They
found that the MB students relied on predictive information that they were given to solve
problems and followed textbook procedures for making decisions. Expert entrepreneurs on
the other hand under-weighted, ignored and even explicitly argued against taking predictions
seriously, working instead with things within their control even if that meant changing their
initial goals and visions for the venture. These two approached overlapped relatively well
respectively with the causal and effectual framework, suggesting that expert entrepreneurs are
more likely to use an effectual approach and novices will likely choose to use a causal logic.
2.6.3 Definitions from the literature
Since this thesis wants to examine the effects of expertise on entrepreneurial decision making
and its effect on entrepreneurial success, the question becomes that of how to define an
expert. Expertise can be defined as sustained superior performance (Dew et al. 2009),
achieved through matching tasks and suitable corresponding decision frames (Gustafsson,
2006). Many authors on the subject in the field of entrepreneurship agree that ultimately
expertise is some function of temporal experience coupled with deliberate practice (Ericsson
et al., 2006; Mitchell et al. 2000; Read & Sarasvathy, 2005; Sarasvathy, 2009). Some other
factors can also play a role, such as the number and type of problems encountered,
metacognition and communication with other experts (Mitchell et al. 2005).
Sarasvathy (2009) defines expert entrepreneurs as, “a person who, either individually or as part of a team, had founded one or more companies, remained a fulltime
founder/entrepreneur for ten years or more, and participated in taking at least one
company public (p. 21)”. In a similar sense, Dew et al. (2009) take a definition as “expert entrepreneurs are persons who, either as individuals or as part of a team, have founded one or
more companies, remained with at least one company that they founded for more than ten
years and taken it public’’ (p. 294). They add a note of caution that expertise is not merely
experience and that expertise when approached using the simple construct of experience, the
connection with performance weakens considerably, which is supported by Ericsson and
Smith (1991). Mitchell et al. (2000) argue that the distinction between experts and novices
can be made based on the questions “I have started three or more businesses, at least one of which is a profitable ongoing entity’’ and “I have started at least one business that has been in existence for at least two years.’’ Baron and Ensley (2006, p. 1335) let entrepreneurial
networking organizations decide whether an entrepreneur is considered an expert.
Nevertheless in their study experienced entrepreneurs had started on average 2.6 ventures
with an average lifespan of 4.8 years of existence.
2.6.4 Conclusion
In sum, experience and deliberate practice lead to the possession of knowledge structures and
scripts that apply to a given domain. Expertise consists of the ability of matching these scripts
with the problems in a situation, and thereby achieving sustained superior performance. This
could be considered a reasonable approximation towards a measurement of expertise,
although not unproblematic. Difficulties in measuring constructs like deliberate practice, the
amount of experience and type of experience required to achieve expertise, other contributing
factors such as motivation make a clear definition subject to discussion. Also, the importance
of sustained superior performance which Dew et al. (2009) stress, might lead to the question
of how such superior performance would look like to an expert in the field of
entrepreneurship. That the notion of entrepreneurial performance is by no means evident in
the literature will be the subject of the next section.
2.7 Entrepreneurial outcomes - a holistic notion
2.7.1 Introduction
So far several theoretical concepts which influence the entrepreneurial decision making
process have been analysed. However applying an effectual or causal logic is not a goal in
itself, but rather a way to reach a certain outcome (Sarasvathy, 2009). In business literature
such an outcome is often translated in terms of performance or success, such as financial or
operational indicators (Venkatraman & Ramanujam, 1986). In the traditional perspective
entrepreneurship slightly different measures are often taken, focussing on those which relate
best to new venture performance (Chandler & Hanks, 1993).
2.7.2 Dependent variables in entrepreneurship research
Determining the dependent variable in entrepreneurship research however, is considered
problematic (Cohen & Mitchell, 2008). In a study done by Murphy et al. (1996) it is argued
that in many cases, the generalization of empirical findings across performance indicators is
not justified. They found evidence that the relationship between a given independent variable
and performance is likely to depend upon the particular performance measure used. It is quite
possible for an independent variable to be positively related to one performance measure and
negatively related to another. This means that even though solid research is done, the
outcomes may be positive or negative based on the selected performance indicator. Figure 2.7
provides an example of the wide range of possible performance indicators (Murphy et al.,
1996).
Figure 2.7: List of performance measures, based on Murphy et al. (1996).
Another thing that this table shows is the reliance on financial indicators to measure
performance. This is also recognized by Cohen & Mitchell (2008) who argue that
entrepreneurship researchers have yet to explore the full range of variance that occurs
in entrepreneurial value creation. The pursuit of entrepreneurial rents as the primary driver
for aspiring entrepreneurs has thereby resulted in the exclusion of other drivers and impacts
of the entrepreneurial activity (Amit et al., 2000). Cohen & Mitchell (2008) even go as far as
stating that the field of entrepreneurship is stuck in a Kuhnian state of normal science, and
that a paradigm shift is required in looking at the way value is created by entrepreneurial
activity to advance the field in a much needed new direction. A similar point is made by
Murphy et al. (1996) who argue that treating performance as a unidimensional construct will
stall entrepreneurship research.
Therefore the aim is to expand on the notion of entrepreneurial performance, or success, and
attempts to come up with a multidimensional construct that takes different perspectives on
entrepreneurial outcomes into account. Following Davidsson (2016), outcomes of
entrepreneurial activity are taken to be relevant on a spectrum, which stretches from an
individual’s intention to engage in entrepreneurship to success at establishing a new venture in the market to the societal level impact of entrepreneurial endeavors. Developing a
performance construct that takes individual, societal and firm level considerations into
account serves two purposes. First it addresses the concerns of Murphy et al. (1996) about the
generalizability of performance indicators. Secondly it is in line with the effectual view on
entrepreneurship, which considers entrepreneurship as more than a planned approach to
maximize rents. This second aim can be seen as supporting the attempt to advance what some
consider a paradigm shift in the way entrepreneurship research is approached (Blenker et al.,
2011; Cohen & Mitchell 2008; Perry et al., 2011; Sarasvathy, 2001). Such a new paradigm
would challenge existing causal notions on what it means to be an entrepreneur, and a
corresponding success construct would go beyond notions of exclusively financial
performance.
2.7.3 Succes in effectuation research
In her seminal article about effectuation, Sarasvathy (2001: p.248) talks about how
effectuation differs from causal reasoning by presenting the example of U-Haul. If one would
present this example as a case study to students who would use causal logic, they would
conclude that the project would not be successful based on either financial or psychological
reasons. It seems that rather than financial returns, Sarasvathy considers a successful outcome
of effectual decision making things that lead to the creation of a new venture, where other
types of decision making processes would not have seen such possibility. Opportunities are
not discovered but rather successful entrepreneurs create them.
In a similar manner she argues in the concluding remarks to give up notions of objective
criteria to measure successful personalities or businesses (Sarasvathy, 2001: p.258). Instead
we need to be sensitive for local and individual differences in markets and entrepreneurs.
Rather to ask how to build a good business or how to become a accomplished entrepreneur,
we should look to personal circumstances and contingencies of individuals to see what types
of opportunities are suitable for the given situation. This means to disconnect the success of
the individual entrepreneur from the success of the firm he or she creates. A similar argument
is made by Sarasvathy, Menon & Kuechle (2013), which will be explored later on in this
chapter.
In a meta-analytic review of effectuation and venture performance, Read et al. (2009)
compared the use of effectuation principles with new venture performance on 48
entrepreneurship studies. Performance measures ranged from revenue per employee at the
first round of financing, to revenue growth, return on assets (ROA), firm size, net interest
margin, and productivity. The authors try to eliminate possible biases by reducing subjective
measures and running a comparative analysis. However they acknowledge that these results
only provide some assurance that they were not biased by performance measures that reflect
too broad a set of outcomes, and Murphy et al. (1996) show us that the bias in their
independent variable would most likely still be substantial.
Using furthermore a search string with the words effectuation, success, performance and
dependent variable, several papers were found which addressed the operationalized notion of
new venture performance or success from an effectual perspective. Most focus on financial
indicators such as profit and sales (Song et al. 2008), the internal rate of return (IRR) for
angel investors (Wiltbank et al., 2009), growth, profitability and survival (Brinkmann et al.,
2010), sales growth, market share growth, and profit growth (Laskovaia et al., 2017). One
recent study included next to a host of financial indicators also a subjective comparison with
direct competitors (Deligianni et al., 2017). Only a single study did not rely on a financial
indicator but chose for the degree to which outcomes aligned with company vision as a
notion of success (Wiltbank et al., 2006).
2.7.4 Multiple dimensions
Murphy et al. (1996) conclude that valid conclusions regarding the outcomes of
entrepreneurship would preferably require a multidimensional construct. Ideally such a
construct would not only take financial performance indicators into account (Cohen &
Mitchell, 2008), but also more contingency based factors that cause individuals to become
entrepreneurs. From an effectual perspective, such factors would also be likely to play a role
in notions of entrepreneurial success (Sarasvathy, 2001). For this reason, the remainder of
this section is devoted to exploring the possibility of a more holistic conception of
entrepreneurial outcomes.
In their analysis of serial entrepreneurship as a temporal portfolio, Sarasvathy, Menon &
Kuechle (2013) offer a valuable starting point for a multidimensional view of success. They
do this by arguing for a separation of the entities entrepreneur and firm. What constitutes a
failure as a firm, might be a success in terms of learning experience for the entrepreneur.
Since the entrepreneur has the opportunity to form a portfolio of multiple firms, failure at one
firm might lead to increased performance at others. This means that success from the
perspective of the entrepreneur is different from success from a firm perspective. Combining
this insight with the common approach for distinguishing different levels of analysis in the
social sciences (Babbie, 2013), success of an individual entrepreneur can be classified as
occurring on a micro level, and a firm level investigation would constitute a meso level
analysis.
A good basis for understanding what exactly constitutes a desired outcome for the
entrepreneurial individual is the empirical analysis performed by Fisher et al. (2014). Their
literature study supports the line of thinking by Sarasvathy, Menon & Kuechle (2013), that
entrepreneurial success is typically understood through the context in which it is found. As a
result, academics, policy makers and entrepreneurs can have different perspectives on what it
means to be successful (Fisher et al., 2014). Taking as their starting point the entrepreneur’s own perception, the study found that the construct entrepreneurial success is a combination of
personal and business performance indicators. It includes the entrepreneur’s feelings of satisfaction and personal expectations for their life and business, combined with continuous
business growth and exceeding business goals.
Figure 2.8: Entrepreneurial success factors, based on Fisher et al. (2014).
Thus, Fisher et al. (2014) argue, entrepreneurial performance is “a hybrid of individual success and the success of entrepreneurial activities; it is a multidimensional construct’’ (p. 488). Their scale captures the micro level measurement constituted by the individual
entrepreneur. Furthermore, their personal factors include also the financial performance
indicators growth and exceeding established business goals. It is argued here that these
performance indicators count as an adequate measure of meso level success since they are in
line with commonly used firm level performance indicators in the literature (Amit et al.,
2000; Cohen & Mitchell, 2008; Murphy et al., 1996; Read et al., 2009).
Building then on the conception of entrepreneurship as outlined in the introduction, the
collective efforts of entrepreneurs are an influencing force stretching also beyond the
individual or organisational level. Such a macro level unit of analysis is often taken to be
constituted by society (Babbie, 2013). The most common understanding then is that in
modern societies, entrepreneurship is widely seen as a key source of economic growth and
welfare increases (Dew & Sarasvathy, 2007). Not surprisingly, entrepreneurial activity is
empirically proven to influence factors such as employment and job creation (Block et al.,
2017; Carton et al., 1998), productivity growth and innovation (Praag en Versloot, 2007).
The macro level notion of successful entrepreneurship is thus reflected by broader economic
variables that span across society. Employment levels could be considered one of the more
popular topics in contemporary discourse on the economic status of society. Following Poh
Kam Wong et al. (2005) who show that one of the most important factors for economic
growth is job creation, and Davidsson (2016) on desirable dependent variables for
entrepreneurship research, the number of created jobs is to be considered an important
indicator for entrepreneurial success on a societal level. Furthermore, following Montiel and
Delgado-Ceballos (2014), income inequality between employees and environmental impact
are important indicators for desired societal outcomes of entrepreneurship
2.7.5 Conclusion
To sum up, the current notion of measuring desired outcomes in entrepreneurship research is
not without flaws. Often used unidimensional constructs lack the ability to be generalized,
which is problematic. Besides, there is a consistent focus on financial indicators, leaving out
possibilities to assess entrepreneurial outcomes on other fronts. An overview has been made
of the use of entrepreneurial performance in the effectuation literature. Recognizing the need
for adequate performance measurements that are suited to a possible changing view on the
nature of entrepreneurship, an attempt is made to build a more holistic notion of
entrepreneurial outcomes. This construct includes an individual, firm, and societal
perspective. It includes financial indicators, individual entrepreneurial satisfaction,
environmental and socio-economic factors. Operationalization of this construct can be found
in chapter four, and will hopefully be more aligned to the effectual school of thought.
Chapter 3. Model & Hypotheses
“Prediction is very difficult, especially about the future.”
~Niels Bohr
The aim of the thesis is to perform an empirical validation of contextual factors affecting the
use of entrepreneurial decision making models, and to introduce a multidimensional measure
of success. To achieve those goals, this chapter is devoted the development of the hypotheses
that form the basis for empirically testing assumptions related to context and success. The
hypotheses and the subsequent conceptual model are based on the theoretical framework
discussed in the previous chapter.
According to some authors, entrepreneurship is primarily a mode of thinking, and decisions
to start a new business are in fact subject to an underlying cognitive order (Mitchell et al.,
2000;2002). Such a cognitive order might be found in the distinction between intuitive and
rational thinking styles (Epstein et al., 1996). Therefore the relationship between these two
thinking styles and the choice for an entrepreneurial decision making model is captured as:
H1: Entrepreneurs who have an intuitive thinking style are more likely to use effectual
reasoning than those who have a rational thinking style.
The conception of culture was made as being tight or loose (Gelfand et al., 2006; 2011, Uz,
2015). Tight cultures value stronger norms and reject deviant behavior from those norms. It is
hypothesized that stronger norms and less tolerance for deviant behavior results in a desire
for a planned, i.e. a causal approach. Mitchell et al. (2000:20002) argue furthermore that the
influence of culture on the relation between cognitive scripts and entrepreneurial decision
making is likely to be moderating in its effects (see also figure 2.3). This relation is tested in
the following hypothesis:
H1a: A high perceived cultural tightness of society has a negative moderating effect on the
relation between intuitive thinking style and effectual reasoning
Being an expert is tied to situational specific domains (Dew et al., 2009), and is seen as a
heuristic (Read & Sarasvathy, 2005) or possession of scripts (Mitchell et al., 2000) that allow
entrepreneurs to match a task with a suitable decision model (Gustafsson, 2006). In order to
gain a deeper understanding of how expertise influences the decision making of
entrepreneurs, two hypotheses are tested. First the possession of expert knowledge structures
is tested for a moderating influence on the relation between cognitive thinking style and
choice for decision model. Dew et al., (2009) already tested the direct relation between
expertise and entrepreneurial decision making. However a methodological weakness in their
study concerned generic MBA students being defined as novices. This thesis contributes by
an empirical validation based on a better operationalization of novice entrepreneurs. These
two hypotheses are formulated as:
H1b: Being an expert has a positive moderating effect on the relation between intuitive
thinking style and effectual reasoning.
H2: Expert entrepreneurs are more likely to use effectual reasoning than novice
entrepreneurs.
Applying an effectual or causal logic is not a goal in itself, but rather a way to reach a certain
outcome (Sarasvathy, 2009). Effectuation theory claims that people who are good at
entreprneurship, experts, are more likely to use effectual principles in their decision making.
This implies that the use of effectuation should lead to better entrepreneurial outcomes than
the use of causation. Therefore effectuation theory only makes sense when it leads to more
succes. The effect of effectual reasoning on a broad notion of entrepreneurial succes is thus
tested:
H3: The use of effectual reasoning is more likely to lead to entrepreneurial success than the
use of causal reasoning.
The theory and hypothesis are summarized in the conceptual model, shown in figure 3.1
below. For clarity, it is chosen to visualize one aspect of the concepts, i.e. intuitive thinking
instead of cognitive style, because this allows to represent the direction of the expected
effects, being positive or negative. This means that, in line with the use in other research, the
concepts of thinking style, cultural tight-looseness and decision style are treated as if they
were dichotomous and antagonistic. In practice such clear distinction might not exist,
meaning that intuitive thinking does not per se exclude a more rational thinking, and that
effectuation can be used simultanously with causation. However for research purposes this
distinction is assumed. In the discussion this fact is taken into account and reflected upon.
Figure 3.1: The conceptual model.
Chapter 4. Methodology
“Science is made up of mistakes, but they are mistakes which it is useful to make, because
they lead little by little to the truth.” ~Jules Verne, Journey to the Center of the Earth
4.1 Introduction
Following the literature review and model construction in the previous chapters, we will now
turn to the methodology. Ideally the appropriate research design and subsequent methods
come from and are thus in line with the literature review (Kothari, 2004). Although much
new knowledge is derived from qualitative methods such as the ‘think aloud protocol’ (Dew et al., 2008; Sarasvathy, 2009), this thesis will follow up on recommendations made by other
authors that effectuation theory needs more robust theory testing by means of quantitative
analysis (Arend et al., 2015; Perry et al., 2011).
The research design will therefore be primarily quantitative, and make use of survey
instruments. Following the hypotheses, expert entrepreneurs will be aimed for as respondents
in the survey. The main theoretical constructs are operationalized by using survey
instruments developed and validated in the literature. The constructs of expertise and success
will be of particular importance since they are developed based on a combination of different
theoretical contributions. The validity and generalizability are discussed, and the conclusion
of this chapter contains a short reflection on the strengths and weaknesses of the
methodological approach.
4.2 Research Design
Research design is the framework that has been created to find answers to research questions
(Creswell, 1994). Within that framework, social science researchers ask primarily one of two
fundamentally different questions. The first is framed as ‘what is going on?’ and is classified
as descriptive research, the second goes into ‘why it is going on?’, and is viewed as explanatory research (de Vaus, 2001). The current research aims to describe factors
influencing entrepreneurial decision making, and by doing so tries to explain why some
entrepreneurs use a specific decision method and how this leads to entrepreneurial success.
The research design is therefore quantitative and explanatory. Following the classification by
Vaus (2001), the methods that are used in this study fall into the category of a causal design.
It seeks to determine causal relations between precursors of entrepreneurial behaviour and
actual behaviour, and if there is a causal relationship between entrepreneurial decision
making type and entrepreneurial success. The hypotheses are tested by statistical analysis
using SPSS to test for these relations.
4.3 Sample
The population
As part of the methodology, a suitable set of respondents should be defined. Sampling is the
act of defining a subset of the total population for which inferences are made. If the subset
represents the relevant properties of the total population, inferences about the subset can be
generalized to the total population (Babbie, 2013). The first step is thus to define the total
population. There were two options to do this. First there is a possibility to take entrepreneurs
in the Netherlands as the total population. Since most respondents are Dutch, this is the
obvious choice. There are some limitations resulting from the sampling strategy so that a
higher portion of the respondents lives in a certain geographical area and thus are somewhat
overrepresented. However the assumption is made that interprovincial differences between
entrepreneurs in the Netherlands are not substantial.
Another possibility for defining the population stems from the literature review. Following
the line of argument by Mitchell et al. (2000;2002), it is possible to frame entrepreneurship
as a special type of cognitive mindset. This means that entrepreneurs, facing roughly similar
challenges in their daily operations, share enough cognitive features that they can be grouped
within a global culture of entrepreneurship. This would mean that research into
entrepreneurial decision making can be generalized across national boundaries. In that case
the total population can be defined as entrepreneurs around the world. However that seems
like stretching things a bit to far, and it might be better to be prudent. Therefore the total
population is defined as entrepreneurs across the Netherlands.
Sample size
Sampling error is often taken to be the difference between the sample mean and population
mean (Cohen, Manion, and Morrison, 2013). A variation between the sample and the
population can occur due to the chance selection of different individuals. To minimize the
effect and occurence of sampling error, the sample size should contain sufficient respondents.
Cohen et al. (2013) specify a range so that a sample of below thirty would be considered
precarious, and for sample sizes of eighty and above there would be little effect on the
sampling error. The obtained sample size of eighty seven entrepreneurs should therefore be
enough to minimize sampling error effects.
Sampling procedure
After defining the proper population, there are several ways to perform a sampling procedure.
Babbie (2013) distinguishes between two main techniques, that of probability and
nonprobability sampling. Probability sampling gives a random sample from the total
population, and has the advantage that it makes sure the sample is representative of the
population. Nonprobability sampling can be used in cases where information about the total
population is difficult to retrieve, or when it is appropriate to select a sample on the basis of
certain elements of the population combined with the purpose of the study (Babbie, 2013)
For this thesis a combination of purposive and snowball sampling is used. Purposive
sampling allows to leverage access to an existing network of expert entrepreneurs. These
entrepreneurs are active in different industries, and are different in terms of age and
entrepreneurial experience, although as noted specific geographies are slightly
overrepresented. Because of the personal connection, response rates were probably higher
than a impersonal approach. Another advantage of this strategy over a probability sample
consisting of Chamber of Commerce data on entrepreneurs is that in the latter there is a
possibility of differences between likelihood of responding to an email questionnaire. One
could imagine that those entrepreneurs with more time on their hands would reply to an
emailed questionnaire, and thereby creating bias.
Nevertheless the purposive sampling strategy using the author’s personal network yielded no higher response rate than roughly fifty percent. Even after two reminders it seemed that
filling in the questionnaire was not top priority for everyone. Therefore several other channels
had to be used. Snowball sampling was employed to ask specific people, including the
supervising professors, friends and family in the author’s network to send a ready made text to the entrepreneurs they knew. An internal email was sent by the CeeSpot community,
business club JongGedaan and TEC Twente to their members. The Nesst Foundation sent a
request to participate to all its alumni and mentors.
Furthermore a call for respondents was made to entrepreneurial Linkedin groups and Twente
University alumni networks. The forum Higher Level and the Facebookgroup Young
Creators, both having a considerable entrepreneurial member base, had the policy to not
allow questionnaires. To circumvent this, a post was created to inform the members of those
communities about the latest academic insights into entrepreneurial decision making in order
to create a win-win situation. Sadly, the moderators could not be convinced to post it.
4.4 Operationalization & instruments
Now that the population is defined and a sampling procedure have been established, it is time
to turn to the operationalization of the relevant theoretical constructs as identified in the
literature. Following the conceptual model as shown in figure 3.1, there are five theoretical
constructs that need to be operationalized in order to be able to measure them.
Thinking style
Some of the most interesting work on entrepreneurial cognitions is done by Mitchel et al.
(2000). However as was noted in the theoretical framework, their operationalizations using
cognitive scripts is not without drawbacks. A major problem for example is the inability to
directly measure the degree of mastery of cognitive scripts since they are, as mental
operations, not directly observable (Mitchell et al., 2000; p.982). Cognitive-experiential self
theory (CEST) as developed by Epstein (1994), with its dichotomous distinction between two
types of information processing modes, fits the purpose of this study relatively well.
Therefore the validated scales designed by Epstein (1994; 1996) are used to operationalize
the construct of cognitive thinking style.
Thinking style is then measured by ten questions with scores range from one till five on a
Likert-type scale. From the total ten questions, the first five of them cover the Need for
Cognition (NFC) and question six till ten cover Faith in Intuition (FI). Questions one, two
and five have been subject to reversed coding so their scores have been reversed. The mean
of these two constructs is calculated using SPSS. The total mean is then divided by the
amount of answer possibilities, in this case five, to ensure comparability between the
constructs that have different Likert-type scales. The results are two variables NFC and FI
with a mean score ranging between zero and one. The Cronbach’s alphas for NFC and FI were satisfactory, being .734 and .747 respectively. Factor analysis showed two components
with cumulative loadings of .49 and .64.
Culture
The cultural aspects are defined by a tight/loose distinction as suggested by the work of
Gelfand (2006; 2011). From his work there are also validated measurement instruments
available which measure the theoretical construct of national culture, which are used as part
of the construction of the survey.
Cultural tightness is measured by six questions with scores range from one till six on a
Likert-type scale. Question four is reverse coded and thus reverse scored, and added to the
scores of the other five questions of culture. The total mean is then divided by the amount of
answer possibilities, in this case six, to ensure comparability between the constructs that have
different Likert-type scales. The result is a variable CulturalT which reflects the degree of
cultural tightness with a mean score ranging between zero and one. It reported a Cronbach's
Alpha of .712. Factor analysis reported one component with a cumulative loading of .42.
Expertise
The proponents of effectuation view entrepeneurs as either novice or expert, with a clear
tendency to view expertise as resulting from experience (Dew et al. 2009; Read &
Sarasvathy, 2005; Sarasvathy, 2008). Even though some reckognize that this approach might
have limitations, for example Dew et al. (2009) issue a warning that only taking experience
into account will not lead to an optimal measurements, they do not include this idea in their
operationalization.
A dichotomous approach to expertise must lead to an arbitrary cutoff point between novice
and expert entrepeneurs. A conception of expertise as occuring along a scale might be more
adequate to describe reality. Therefore expertise in this thesis is operationalized as a variable
based on four criteria to which weights are assigned based on the definitions from the
literature. In table 4.1 below the four characterstics of expertise as described in the theoretical
framework are presented, and for each time such characteristic is mentioned in a definition
from the authors, the weight is increased by one point.
Expert criterium Literature
reference
Category Score Weight
Years Entrepreneur1 Sarasvathy
(2009)
Dew et al. (2009)
Mitchell et al.
(2000)
Baron and Ensley
(2006)
1 year
2 years
3 years
4 years
5 years
6+ years
.5
1
1.5
2
2.5
3
4
Started more than 1
company
Sarasvathy
(2009)
Dew et al. (2009)
Mitchell et al.
(2000)
Baron and Ensley
(2006)
No
Yes
0
1
4
Education level
Ericsson & Smith
(1991)
Gustafsson
(2006)
Other
Bachelor
Master
PhD
0
1
2
3
2
Entrepeneurial
courses
Ericsson & Smith
(1991)
Gustafsson
(2006)
No
Yes
0
1
2
Table 4.1. Operationalization of expertise
Decision style
Decision style is measured by ten questions with scores range from one till seven on a Likert-
type scale (Alsos et al. 2014). From the total ten questions, the first five of them cover the
Causal Decision Style (CDS) and question six till ten measure Effectual Decision Style
(EDS). The mean of these two constructs is calculated using SPSS. The total mean is then
divided by the amount of answer possibilities, in this case seven, to ensure comparability
between the constructs that have different Likert-type scales. The results are two variables
CDS and EDS with a mean score ranging between zero and one. The Cronbach’s alphas for CDS and EDS were satisfactory, being .751 and .808 respectively. The factor analysis was
also acceptable, albeit not with a big margin, and showed two components with cumulative
loadings of .37 and .54.
1 From 65 entrepeneurs there was no data available on how many years they already were entrepeneur. However
all of them were the founder of their first startup, and the age of this startup ranges between 0 and 4 years.
Therefore the assumption is made that the age of the startup reflects the number of years experience of the
entrepeneur.
Success
The components for this construct were described in the theoretical framework. It includes
entrepreneurial success factors as identified by Fisher et al. (2014) for the micro and meso
level measurement, using the instrument that was validated in that study. Job creation,
environmental impact and income equality constructs are used for the macro level
measurement, and are operationalized respectively by measuring the number of jobs created
by the company, the effects of business operations on the environment, and the degree of
income inequality between employees (Montiel and Delgado-Ceballos, 2014; Poh Kam
Wong et al., 2005).
The resulting five theoretical concepts and their corresponding measurement instruments are
combined into a single survey instrument.Six of the questions are measured using a one till
five points Likert scale and so can be added to one another. The amount of jobs are
categorized in five sections in order to allow this data to be integrated. One employee is
considered not successful in terms of additional job creation and is given a score of one. Two
employees means that the company offers a small benefit to society so is given a score of
two. A score of three is given to companies that have created between three and nine jobs.
Employing between ten and forty nine people is considered relatively successful in job
creation so is given a four out of five score. Fifty or more employees is considered to have
substantial benefits in terms of employment for society so those companies are given a score
of five. This results in a new variable called EmploymentScore.
The mean of the total scores is is then divided by the amount of answer possibilities, in this
case five, to ensure comparability between the constructs that have different Likert-type
scales. The result is a variable ESuccess which reflects the degree of entrepreneurial success
with a mean score ranging between zero and one. Additionally because several items were
included into an already existing scale, another variable, ESuccessI was made consisting only
from the questions from the validated scale by Fisher et al. (2014).
To test for the internal consistency, the Cronbach’s Alphas were calculated. The variable ESuccessI reported a value of .652. The Cronbach’s Alpha for the scale developed by Fisher et al. (2014) was .71 in their original study, but that number was not replicated here and care
should therefore be taken since it is slightly below the accepted .70-.90 range (Tavol &
Dennick, 2011). Factor analysis was performed and revealed two factors with a cumulative
loading of .51 and .77, suggesting that in fact two concepts were measured.
The Cronbach’s Alpha for ESuccess reported a value of .551 which is significantly lower than the accepted range. Factor analysis revealed three factors with cumulative loadings of
.32, .55 and .67, meaning multiple concepts are in fact measured. Instead of discarding the
construct, it is argued that it can nevertheless be included in this study, taking some caution
and prudence for the use into account.
Cronbach’s Alpha is affected by test length and dimensionality, and can be used to confirm whether or not a sample of items is actually unidimensional (Tavol & Dennick, 2011).
Shorter tests which measure multidimensional constructs can therefore be expected to have
lower Alphas. The success construct deliberately tries to include multiple dimensions into a
single measure. These dimensions, such as environmental impact are only measured by a
single question. The nature of the construct therefore elicits a low Cronbach’s Alpha, and while internal consistency is low, the decision is made to carry on with the construct because
one of its aims was to broader the measurement of success to include multiple factors. That
being said, the conclusions should be assessed critically, and it is clear that this notion of
success can only be used for exploratory purposes. For more future research the construct has
to be developed further, which was also in line of the expectations. A more detailed reflection
on this can also be found in the discussion.
Control variables
In order to control for effects that might be considered to be an underlying explaining factor,
several control variables are included in the data analysis. The focus is primarily on historical
contingencies that can influence the choice for effectuation or the level of expertise. These
include the level of education, completed courses on entrpeeneurship, whether the indivdual
had entrepeneurial parents from which he or she could learn. The reason to start a business
might also influence the type of decision making. Furthermore gender is included to see if
any differences between males and females (no other classifications were given by the
respondents) exist. Finally experience might come trough age, and this could possibly effect
expertise.
Gender
Gender is included as a control variable, not because theory indicates likely differences
between males and females, but because data is collected and it could be interesting to see
what its effects are.
Age
As experience in a variety of areas increases with age, it might have an effect on the level of
expertise and is therefore included.
Education
The level of education, operationalised as being either PhD, Master, Bachelor or other type of
education. The level of education can be expected to influence the choice for a causal or
effectual decision style, at the minimum because causal methods are thaught in business
schools (Sarasvathy, 2001), and the development and degree of expertise.
Entrepeneurial education
Having followed courses related to entrepeneurship is considered to be classified as
entrepeneurial education. This means that the entrepreneur did have at least some contact
with theory. Having knowledge about entrepeneurship theory might influence entrepeneurial
decision making or the development and degree of expertise.
4.5 Procedures
Data collection
The survey was administered using Google Forms. The introductory text is written to
stimulate participation but does not entail too much information about the study in order not
to create biased answers. The survey is distributed online, mostly by providing a link to the
form via email, Whatsapp or Linkedin. Personal and company name are asked in order to
track response rates, since Google Forms does not allow to track responses based on IP
addresses. However the option is given to complete the survey anonymously since especially
entrepreneurs from larger companies do not want to give insight in their ways of working
without the guarantee of anonymity. At the end there is a possibility to leave an email address
to receive results of the study. This was done as a gesture to create goodwill and to increase
response rate.
Data analysis
The constructs that have been measured use different Likert scales. For the sake of
comparability, all mean scores resulting from these scales have been divided by the by the
number of possible answers. This results in a measurement scale for all constructs ranging
from zero to one. The only problem here might be that a respondent could respond slightly
different to differences in ranking scales (Colman, Norris & Preston, 1997). To make sure
this adjustment gave similar results, a comparison was made on the correlation between
effectual decision style and entrepreneurial success for both the normal and Likert point
adjusted constructs. The correlation was the same, indicating that adjusting for the amount of
answering options did not influence the effect of the relation but does allow for better face
value understanding of the means.
Furthermore, the results of the analysis are classified in terms of correlation strength. For this
the classification of Evans (1996) will be used which categorizes correlations according to
the following definitions:
00-.19 “very weak”
.20-.39 “weak”
.40-.59 “moderate”
.60-.79 “strong”
.80-1.0 “very strong”
4.6 Validity
Internal validity
Internal validity can be defined as the validity of inferences about whether the observed
covariation between the treatment and outcome reflects a causal relationship (Shadish, Cook
and Campbell, 2002). It is the extent to which a causal inference is justified, which is
determined by the degree of systematic error, or the difference between measured value of a
quantity and its true value (Brewer, 2000). It is the approximate truth about inferences
regarding causal relationships.
The issue of causality is one of the most challenging for this study. Care has to be taken to
ensure that it is really causation and not mere covariation that is measured. Empirical
association, or correlation, is expected based on the literature study. The appropriate time
order follows to some extent logically, as entrepreneurial decision making should come
before entrepreneurial success. For the other variables this relation is not logically
straightforward, for example expertise can lead to effectual decision making, but it might also
be the other way around. The assumption about these relations and their time order are made
based on the literature, see for example Dew et al. (2009) or Read & Sarasvathy (2005). The
main challenge however comes from the causality criterium that the relationship should be
nonspurious (de Vaus, 2001).
Construct validity
Construct validity refers to the degree to which inferences can be made from the
operationalizations in a study to the theoretical constructs on which those operationalizations
were based (Trochim, 2006), or the ‘’validity of inferences about the higher order constructs that represent sampling particulars’’ (Shadish, Cook and Campbell, 2002). Simply stated this means that construct validity boils down to being a degree to which the instruments indeed
measure the theoretical constructs. Ideally these should correspond perfectly, since by
operationalizing the theoretical construct the instrument is determined.
As the operationalizations for effectuation, entrepreneurial cognition and culture involved
well validated scales from peer reviewed journals, which took several measures to ensure
construct validity on their own, the instruments for measuring these constructs are assumed to
have an adequate degree of construct validity. As already state in section 4.4,
operationalization of expertise is challenging. This is mostly because the theoretical concept
of expertise is not well defined and understood in the literature. Since it is not exactly clear
when someone is an expert, or what the path is to become one, an operationalized construct
of expertise is inherently prone to ambiguity. A mean definition based on several articles is
taken to hopefully increase construct validity on the concept of expertise. Finally, the success
construct is partly made from the tested and validated scale of Fisher et al. (2014). However it
also includes measures on job creation, the environment and income equality. Although these
measure are independently proven to have an impact on entrepreneurial success (Poh Kam
Wong et al., 2005; Montiel and Delgado-Ceballos, 2014), their operationalizations required
simplicity to align to the scope of this thesis. This results in somewhat unsophisticated macro
level indicators of the success construct.
4.7 Ethical considerations
Trochim (2006) identifies several ethical issues in social science research. Voluntary
participation is ensured because the respondents can choose to participate and there is no real
cost for them to decline the request for participation. There has to be the requirement of
informed consent. This is achieved by an introduction into the instruments and use of data at
the beginning of the survey. Even though not too much about the methodology is revealed to
minimize the risk of social bias, it is sufficient to give the respondents a picture of the overall
research and possible risks they are under. There is little risk of harm when participating as
respondent in this survey. One major risk is careless storage and use of data, which could
result in sensitive information about business processing or decision making to leak.
This risk is mostly related to issues of confidentiality and of privacy. In the introduction of
the survey is a statement that the data will be handled confidentially and that it will be made
available for subsequent research within the University of Twente. There is also an option to
fill in the questionnaire anonymously for those respondents who wish to remain anonymous.
Besides that, personal and company names will not be made available to the subsequent
research within the University of Twente. In the case that there is a need for these names or
contact information about a set of questions or respondent, it is possible to contact the author.
Permission to share this data can then be asked to the individual respondent and can be shared
with his or her permission. Finally all respondents are given the choice to provide an email
address so that the results of the study can be given as a bonus for taking the time to complete
the survey.
4.8 Conclusion on methods
In this chapter the research design and the methods were discussed. The aim of this study is
to uncover causal relationships using statistical analysis of empirical data. Such a design has
an advantage over previously often used think aloud protocol analysis (Sarasvathy, 2009), in
that it can better establish causal relations which is needed to support effectuation theory
(Perry et al., 2011; Shadish, Cook and Campbell, 2002). However in this type of research that
is performed at a single point in time, determining causality can be problematic. Furthermore
while the sampling strategy ensures enough respondents for adequate statistical analysis, it
relies on nonprobability sampling which decreases external validity. It is therefore stated that
in terms of methods this thesis is a step in the direction of empirically validating theoretical
constructs relating to effectuation, but that the conclusions have to be evaluated with the
context in mind.
Chapter 5. Results
“Truth has nothing to do with the conclusion, and everything to do with the methodology.”
~Stefan Molyneux
5.1 Introduction
This chapter contains the results of the statistical analyses using hierarchical regression,
which were performed on the available data. The first four hypotheses are tested using the
combined dataset of 272 respondents in section 5.3 Since no additional data was available
that contained information on measures of success as defined by this paper, the hypotheses
on the relation between effectuation and entrepreneurial success is tested using only the
origional collected data in section 5.4. Following these results, chapter 6 will conclude by
answering the research question, discussing limitations and providing an outline for further
research.
5.2 Data description
The first set of hypotheses are tested by examining the relations between thinking style,
culture, expertise and decision style. The descriptives of these variables are shown in table
5.1 below. As can be seen, almost all variables are significantly correlated with one another,
except for expertise and causation.
Table 5.1. Descriptives and correlation matrix.
N Mean Std.
Deviation 1 2 3 4 5
1 Need for Cognition
272 0,72 0,21
2 Faith in Intuition 272 0,71 0,17 0,56**
3 Causation 272 1,67 0,18
0,51** 0,37** 4 Effectuation 272 0,56 0,17 0,2** 0,25** -0,13* 5 Cultural Tightness 272 0,62 0,15 0,57** 0,62** 0,45** 0,28**
6 Expertise 272 12,29 4,54 0,41** 0,32** 0,11 0,21** 0,28** **. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
5.3 Hypothesis testing
This section will provide an overview of the results and the corresponding acceptance or
rejection of the hypotheses. A hierachical regression was performed to give insights into the
effects of the various context variables. Four models are defined. First, only the control
variables are included. Secondly, the relations between thinking and decision style are added.
Then in the third model, culture and expertise are included. In the fourth and final model,
interaction variables are presented in order to test for moderation effects of culture and
expertise on the relation between thinking and decision style. Table 5.2 below shows the
results of the regression analysis.
Table 5.2
Summary of Hierarchical Regression Analysis for Variables Predicting
Effectual Decision Making (N = 272)
Model 1 Model 2 Model 3 Model 4 (Z values)
B SEB t p B SEB t p B SEB t p B SEB t p
Age 0,00 0,00 11,20 0,00 0,01 0,00 -0,07 0,94 0,00 0,00 -1,15 0,25 0,00 0,00 -1,16 0,25
Gender 0,00 0,02 1,15 0,25 0,00 0,02 -0,19 0,85 0,01 0,02 0,26 0,79 0.26 0.21 1.24 0.22
Education Bachelor 0,14 0,04 -0,14 0,89 0,12 0,04 3,13 0,00 0,09 0,04 2,29 0,02 0.22 0.17 1.31 0.19
Education Master 0,07 0,03 3,85 0,00 0,05 0,03 1,75 0,08 0,03 0,03 1,00 0,32 0.19 0.22 0.86 0.39
Education PhD -0,09 0,12 2,31 0,02 -0,05 0,12 -0,43 0,66 -0,07 0,12 -0,58 0,56 0.05 0.15 0.36 0.72
Need for Cognition 0,04 0,07 0,54 0,59 -0,03 0,07 -0,43 0,67 -0.03 0.03 -0.77 0.44
Faith in Intuition
0,18 0,07 2,46 0,01
0,08 0,08 0,98 0,33
0,01 0,01 0,70 0,48
Cultural Tightness
0,25 0,09 2,69 0,01
0.26 0.21 1.38 0.22
Expertise
0,01 0,00 1,76 0,08
0.22 0.17 1.45 0.19
Culture_Intuition
0.19 0.22 0.14 0.39
Culture_Cognition
0.05 0.15 0.64 0.72
Expertise_Intuition
-0.04 0.03 -0.78 0.44
Expertise_Cognition 0,00 0,02 0,11 0,92
Adj. R2 F Df p
Adj.
R2 F Df p
Adj.
R2 F Df p
Adj.
R2 F Df p
0,07 4,09 5;259 0,00 0,08 5,03 2;257 0,01 0,11 5,00 2;255 0,01 0,11 0,69 4;251 0,60
H1: Entrepreneurs who have an intuitive thinking style are more likely to use effectual
reasoning than those who have a rational thinking style.
As we can infer from table 5.2, we see that when Need for Cognition and Faith in Intuition
are added in model 2, the model is significant and explains 8% of the variance on the choice
for an effectual decision style (Adj. R2 = .08, F(2,257), p = .01). Need for Cognition does not
have a significant effect (B = .04, p = .59), and Faith in Intuition does have a significant effect
effect (B = .18, p = .01).
After adding the variables on culture and expertise, model 3 is still significant and explains
now 11% of the variance for an effectual decision style (Adj. R2 = .11, F(2,255), p = .01).
Need for Cognition still does not have a significant effect (B = -.03, p = .67), and Faith in
Intuition lost its significant effect effect (B = .08, p = .33).
It is therefore concluded that although it seems that an intuitive thinking style does have a
significant effect on the use of effectual decision making, this effect is removed when adding
the concepts of culture and expertis. Therefore the hypothesis is partially accepted,
reckognizing that the effects are not robuust enough to remain significant when more
variables are added.
H1a: A high perceived cultural tightness of society has a negative moderating effect on the
relation between intuitive thinking style and effectual reasoning
In model 4 in table 5.2 the interaction variables Culture_Intuition and Culture_Cognition are
included to test for possible moderation effects. However after doing so, the model is not
significant anymore (Adj. R2 = .11, F(4,251), p = .06). Therefore the hypothesis is rejected.
H1b: Being an expert has a positive moderating effect on the relation between intuitive
thinking style and effectual reasoning.
Similar to the moderating effect of culture, the interaction variables for expertise, being
Expertise_Intuition and Expertise_Cognition, are added in model 4. However the model turns
out to being not significant (Adj. R2 = .11, F(4,251), p = .06), so the hypothesis is rejected.
H2: Expert entrepreneurs are more likely to use effectual reasoning than novice
entrepreneurs.
In table 5.2 it can be seen that in model 3 expertise is included as an independent variable to
test its effect on effectual decison making. The model is significant (Adj. R2 = .11, F(2,255),
p = .01). Expertise has no significant effect (B = .01, p = .08) although it is not far from it.
Nevertheless the hypothesis is rejected.
H3: The use of effectual reasoning is more likely to lead to entrepreneurial success than the
use of causal reasoning.
Table 5.3 shows the descriptive statistics for the variables causation, effectuation and
entrepeneurial success. The data set (N = 87) contains only respondents from collected data
and excludes those from the additional dataset as was used for the previous hypotheses. It is
shown that causation is negatively correlated with effectuation (r = -.372, p < 0.01), and that
only effectuation is significantly correlated with Entrepeneurial Success (r = .217, p < 0.05).
Table 5.3 N Mean Std.
Deviation 1 2
1 Causation 87 0,54 0,16
2 Effectuation 87 0,61 0,19
-,372**
3
Entrepeneurial Success
87 0,67 0,12
-0,14 ,217* **. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
The regression analysis in table 5.4 shows then that the model is significant and explains 4%
of the variation of entrepeneurial success (Adj. R2 = .04, F(1;85), p = .04). Effectuation has a
significantly influence on success (B = .13, p = .04), and the hypothesis is therefore accepted.
Table 5.4 Regression results for Effectuation predicting
Entrepeneurial Success
Model 1
B SEB t p
Effectuation 0,13 0,07 2,05 0,04
Adj.
R2 F Df p
0,04 4,22 1;85 0,04
Chapter 6. Conclusion, discussion, future research
“All statements are true, if you are free to redefine their terms.” ~Thomas Sowell
6.1 Interpretation of the results
On the relation between intuitive thinking and effectual decision making
While in a more simplified model a relation was found between intuitive thinking and
effectual reasoning, the effect disappeared when adding variables that measured cultural
tightness and expertise. This indicates that in a simple model the relationship holds, but it is
not robuust enough to remain significant when context variables culture and expertise are
added in the equation. This can either mean that the effect is small, or that culture and
expertise contain underlying effects that influence the relationship. It is an indication that
intuitive thinking has an effect on effectual decision style, however it also indicates the
importance of the context for entrepreneurial decision making.
Furthermore the origional collected data show a negative correlation between the two types
of decision styles, indicating that entrepreneurs who use some form of causal decision
making tend to rely less on an effectual style of decision making. This is also a contested
issue in the literature.
Sarasvathy (2001: p. 245) states that she created the dichotomy between effectuation and
causation only for a more clearer theoretical exposition. She argues that it would not be
logical to strictly follow the divide and simultaneous use of the models can be expected. This
makes sense since a causal approach of strict planning in a controlled environment, as for
example was advocated by Taylorism, is rejected by most scholars (Freedman, 2015). And
when using a reductio ad absurdum, it becomes clear that an entrepreneur cannot use strictly
effectual principles, since he or she would then be only focussing on means and totally
disregarding any goals, or that decisions are made based solely on affordable losses without
considering anything related to expected returns.
Still it is this argument that is used by Arend et al. (2015: p.639) to critique effectuation
theory when they state that it does not consider factors such as competitive forces, thereby
making it lack comprehensiveness as one of the criteria for good theory. However based on a
careful reading of Sarasvathy (2001) and the results of this hypothesis it can be concluded
that the preceding critique by Arend et al. (2015) is invalid when it assumes a strict
separation of effectuation and causation decision styles. For research purposes it might be
beneficial to make such distinction, in practice such strict dichotomy is probably not
productive, and we should be careful to be attentive to this distinction.
On the moderating effect of culture
The literature indicated that culture is an important context variable, and predicted that it has
a moderating effect on the relation between thinking style and decision making (Mitchell et
al. 2000) A correlation analysis showed a positive correlation of both culture and intuitive
thinking style and culture and effectual reasoning. When testing for moderation, it was found
that the model became not significant after including the interaction terms so the hypothesis
was rejected. From this data it can therefore not be concluded that there is a moderating effect
of culture on the relation between intuitive thinking and effectuation. Although there seems to
be some kind of relation between culture and the concepts of intuition and effectuation, the
effect of moderation as predicted by the literature is not found.
On the influence of expertise on effectuation
The proponents of effectuation (Dew et al. 2009; Sarasvathy, 2008) posit that expertise has a
defining influence on the choice for the entrepreneurial decsion model, putting forward the
claim that experts rely more often on effectual principles for their decision making. This
claim attributes a certain amount of authority and appeal to effectuation theory, since being
an expert one is considered as knowing better how to perform than a novice would. Therefore
if experts use effecuation more than causation, this means that effectuation would be a better
decision making logic.
However expertise is a complex concept, and much depends on definitions and the selection
of a sample, where Sarasvathy (2008) used primarily entrepeneurs from very large
companies, and Dew et al, (2009) contrasted experts with novices although a novice was just
a MBA student, and not neccesarily an entrepeneur. Besides, they used dichotomous
distinctions between experts and novices. This study attempted to operationalize expertise as
a scale, based on several definitions of the literature. However no effects were found that
expertise has either a moderating effect on the relation between thinking style and effectual
decision making, or any direct effect on an effectual decision style.
These findings indicate that the subject of expertise should be approached with care, both
because it is a complex concept and hard to define, and because the findings contradict the
theory as proposed by the proponents of effectuation. It is likely that different definitions and
conceptions of expertise lead to dissimilar results, so that a critical reflection of the concept
of expertise would benefit the development of effectuation theory.
On the relation of effectuation and succes
Whether or not experts use effectuation more often than novices do, ultimately effectuation is
not the aim in itself. Rather one should only care about differences in decision making if they
lead to differences in outcomes. Outcome in this sense might differ for different stakeholders,
and it might be wise therefore to define outcome more broadly than just financial indicators.
This is in line with effectuation theory as it is opposing a causal approach which relies more
on financial criteria to evaluate outcomes (Sarasvathy, 2001).
Following this line of thinking, the research includes a broad conception of success which,
although can only serve as an indicator because of methodological limitations, nevertheless
might be usefull as a starting point. The findings showed that effectuation indeed has some
effect on a broadly defined notion of entrepeneurial success.
6.2 Conclusion
How do thinking style, culture, and expertise influence the entrepreneurial decision making
process, and what is the predictive value for entrepreneurial success?
A few words to conclude by looking back at the research question and some of the findings
that are interesting in light of the background on which this study was performed. Intuitive
thinking style was found to impact effectual decision making, untill culture and expertise as
context variables were added. This means foremost that the relation between intuitive
thinking and effectuation is not very robust. It might also mean that culture and expertise are
context variables that influence this relation. However since no moderating effect of culture
was found, and no direct and moderating effect of expertise was found, indicating that the
lack of robustness is more probable. Although limited the amount of explained variance,
there is some indication that effectuation leads to entrepeneurial success.
To conclude, this research supports the idea that thinking style and cultural context are
important for explaining the use of effectuation principles in entrepreneurial decision making.
However these concepts are complex and probably overlapping. Disentangling how exactly
these relationships work will be a challenging task. Nevertheless it might be useful to
advance our understanding of entrepreneurial decision making, especially in a global context.
Expertise should be a concept to be used carefully, with attention to definitions and
measurements. Effectuation in itself is promising to increase our understanding about the
inherent ambivalence of entrepreneurship, especially useful to think about new ways of doing
things, and when it is tied to reflection on how we as individuals and society define success.
Chapter 7. Discussion
“Science is a way of thinking much more than it is a body of knowledge.” ~Carl Sagan
How can we situate the findings of this research into the broader debate? Two important
trends in today’s society seem to be a critical questioning of our assumptions about globalization and about economic liberalism. At least in the US, nationalism having its voice
heard through resigning from the Paris climate agreement, important tariffs and trade
barriers.2 In a time where global convergence (Williamson, 1996) is not as self-evident as it
once was, what is the role of a concept such as the global culture of entrepreneurship that
Mitchell et al. (2000) propose? Is such a view based on a world in which the free movement
of goods, capital and people allow opportunities for enterprising individuals to create their
business?
These might be assumptions about context that present local challenges to general theories
explaining entrepreneurial behavior. As the results of this study show, culture, cognition and
historical contingencies that lead to expertise in a subject might not be easily separated, or
even fully understood. In the social sciences, dealing with humans, causality is often quickly
and easily assumed (Joerges, 1999). It might be wise therefore to be prudent, taking the
advice of Arend et al. (2015) and be critical about new theories before accepting them into
our ways on thinking.
That being said, effectuation has a certain charm, an intuitive feeling reflected in the saying
that you can’t learn how to do business in school. Perhaps it is more useful as a heuristic, an
initial challenge to an old paradigm of rationality and the pursuit of rents. Being able to
explain the more mundane considerations beside that of forecasting and cost calculations
would already be quite an accomplishment. This would be the value of effectuation theory if
it is able to do this.
To cross the bridge between theory and practice, to make use of abstract thinking and
intuition, to work with uncertainty but also understand that some aspects of the future can be
planned for. If effectuation is really different from what is being taught in business schools
(Fisher, 2012), including it in the curricula will be a step towards a better understanding. A
combination of approaches can offer valuable insights for teaching people about
entrepreneurship, whether in an university setting or more informal organizations.
2 https://www.forbes.com/sites/davekeating/2018/06/09/trump-ditches-summit-leaving-a-climate-g6/
Limitations and a way forward
Some limitations of this research stem from difficulties in finding entrepreneurs that qualify
as experts and who would allocate their time to participate, resulting in a relatively small set
of expert entrepeneurs in the data. More thorough testing of the hypotheses would benefit
from a larger sample of experts. Furthermore the notion of expertise proved difficult to
operationalize, resulting in suboptimal choices were the literature has no unified answer,
which can lead to bias when comparing the studies. Furthermore the success construct is a bit
simplistic, and did not meet internal consistency criteria, so it could only be used for
explorative inferences. Finally, concepts like cognition, culture and decision making were
assumed to be seperate, while such an artificial separation might not fully represent reality.
What could be a way forward from here? Effectuation is promising, but scholars lack clear
definitions on what effectuation really is, on expertise, and on the relevance of context. The
standard answer would therefore be to expand research, hoping that more data will lead to
better definitions. A fruitful avenue could also be to use effectuation as an heuristic to
challenge existing entrepreneurial and perhaps business frameworks that centre around a
causal logic. If there is any merit in this approach, if it really offers novel insights, such
frameworks will be successfully criticized. If such approach works, it might be that there is
something in effectuation that is valuable to facilitate changing worldviews.
8. Bibliography
Ahlstrom, D., & Ding, Z. (2014). Entrepreneurship in China: an overview. International
Small Business Journal, 32(6), 610-618.
Alsos, G. A., Clausen, T. H., & Solvoll, S. (2014). Towards a better measurement scale of
causation and effectuation. In Academy of Management Proceedings (Vol. 2014, No. 1, p.
13785).
Amit, R., Brander, J., & Zott, C. (2000). Venture capital financing of entrepreneurship:
Theory, empirical evidence and a research agenda. The Blackwell Handbook of
Entrepreneurship, Malden: Blackwell, 259-281.
Archer, G. R., Baker, T., & Mauer, R. (2009). Towards an alternative theory of
entrepreneurial success: Integrating bricolage, effectuation and improvisation. Frontiers of
entrepreneurship research, 29(6), 4.
Arend, R. J., Sarooghi, H., & Burkemper, A. (2015). Effectuation as ineffectual? Applying
the 3E theory-assessment framework to a proposed new theory of entrepreneurship. Academy
of Management Review, 40(4), 630-651.
Aristotle. (1984). Nicomachean ethics. In J. Barnes (Ed.), The complete works of Aristotle.
Princeton University Press.
Alsos, G. A., Clausen, T. H., & Solvoll, S. (2014). Towards a better measurement scale of
causation and effectuation. Academy of Management Proceedings, 14(1), p. 13785
Babbie, E. R. (2013). The basics of social research. Cengage Learning.
Baker, T., & Nelson, R. E. (2005). Creating something from nothing: Resource construction
through entrepreneurial bricolage. Administrative science quarterly, 50(3), 329-366.
Baron, R. A. (1998). Cognitive mechanisms in entrepreneurship: Why and when
enterpreneurs think differently than other people. Journal of Business venturing, 13(4), 275-
294.
Baron, R. A., & Ensley, M. D. (2006). Opportunity recognition as the detection of
meaningful patterns: Evidence from comparisons of novice and experienced entrepreneurs.
Management science, 52(9), 1331-1344.
Blenker, P., Korsgaard, S., Neergaard, H., & Thrane, C. (2011). The questions we care about:
paradigms and progression in entrepreneurship education. Industry and Higher Education,
25(6), 417-427.
Block, J. H., Fisch, C. O., & Van Praag, M. (2017). The Schumpeterian entrepreneur: a
review of the empirical evidence on the antecedents, behaviour and consequences of
innovative entrepreneurship. Industry and Innovation, 24(1), 61-95
Boettke, P. J., & Coyne, C. J. (2009). Context matters: Institutions and entrepreneurship.
Foundations and Trends® in Entrepreneurship, 5(3), 135-209.
Brewer, M. (2000). Research Design and Issues of Validity. In Reis, H. and Judd, C. (eds.)
Handbook of Research Methods in Social and Personality Psychology.
Cambridge:Cambridge University Press.
Brinckmann, J., Grichnik, D., & Kapsa, D. (2010). Should entrepreneurs plan or just storm
the castle? A meta-analysis on contextual factors impacting the business planning–performance relationship in small firms. Journal of Business Venturing, 25(1), 24-40.
Carton, R. B., Hofer, C. W., & Meeks, M. D. (1998, June). The entrepreneur and
entrepreneurship: operational definitions of their role in society. In Annual International
Council for Small Business. Conference, Singapore.
Chandler, G. N., & Hanks, S. H. (1993). Measuring the performance of emerging businesses:
A validation study. Journal of Business venturing, 8(5), 391-408.
Cohen, B., Smith, B., & Mitchell, R. (2008). Toward a sustainable conceptualization of
dependent variables in entrepreneurship research. Business Strategy and the Environment,
17(2), 107-119.
Cohen, L., Manion, L., & Morrison, K. (2013). Research methods in education. Routledge.
Colman, A. M., Norris, C. E., & Preston, C. C. (1997). Comparing rating scales of different
lengths: Equivalence of scores from 5-point and 7-point scales. Psychological Reports, 80,
355-362.
Cook, H. J. (2004). Medical communication in the first global age: Willem ten Rhijne in
Japan, 1674-1676. Disquisitions on the Past and Present, (11), 15-36.
Creswell, J. W. (1994). Research design: Qualitative & quantitative approaches. Sage
Publications, Inc.
Davidsson, P. (2004). Researching entrepreneurship. New York: Springer.
Davidsson, P. (2016). Researching entrepreneurship: conceptualization and design (Vol. 33).
Springer.
De Vaus, D. A. (2001). Research Design in Social Research. London: SAGE
Deligianni, I., Voudouris, I., & Lioukas, S. (2017). Do effectuation processes shape the
relationship between product diversification and performance in new ventures?.
Entrepreneurship Theory and Practice, 41(3), 349-377
Dew, N., Read, S., Sarasvathy, S. D., & Wiltbank, R. (2009). Effectual versus predictive
logics in entrepreneurial decision-making: Differences between experts and novices. Journal
of business venturing, 24(4), 287-309.
Dew, N., & Sarasvathy, S. D. (2007). Innovations, stakeholders & entrepreneurship. Journal
of Business Ethics, 74(3), 267-283.
Dijk, van M. (2012) DJ-cultuur en collectieve causaliteit in de mashupcultuur van de vroege
eenentwintigste eeuw. Een onderzoek naar het werk van Girl Talk en 2manydj’s. Faculteit der Letteren van de Rijksuniversiteit Groningen.
Epstein, S. (1994). Integration of the cognitive and the psychodynamic unconscious.
American psychologist, 49(8), 709.
Epstein, S., Pacini, R., Denes-Raj, V., & Heier, H. (1996). Individual differences in intuitive–experiential and analytical–rational thinking styles. Journal of personality and social
psychology, 71(2), 390.
Ericsson, K. A., & Smith, J. (Eds.). (1991). Toward a general theory of expertise: Prospects
and limits. Cambridge University Press.
Ericsson, K. A., Charness, N., Feltovich, P. J., & Hoffman, R. R. (Eds.). (2006). The
Cambridge handbook of expertise and expert performance. Cambridge University Press.
Fisher, G. (2012). Effectuation, causation, and bricolage: A behavioral comparison of
emerging theories in entrepreneurship research. Entrepreneurship theory and practice, 36(5),
1019-1051.
Fisher, R., Maritz, A., & Lobo, A. (2014). Evaluating entrepreneurs’ perception of success: Development of a measurement scale. International Journal of Entrepreneurial Behavior &
Research, 20(5), 478-492.
Freedman, L. (2015). Strategy: A history. Oxford University Press
Gelfand, M. J., Nishii, L. H., & Raver, J. L. (2006). On the nature and importance of cultural
tightness-looseness. Journal of Applied Psychology, 91(6), 1225.
Gelfand, M. J., Raver, J. L., Nishii, L., Leslie, L. M., Lun, J., Lim, B. C., ... & Aycan, Z.
(2011). Differences between tight and loose cultures: A 33-nation study. science, 332(6033),
1100-1104.
Gelderen, van, M. (2010). Autonomy as the guiding aim of entrepreneurship education.
Education+ Training, 52(8/9), 710-721
Gupta, V. K., Chiles, T. H., & McMullen, J. S. (2016). A process perspective on evaluating
and conducting effectual entrepreneurship research. Academy of Management Review, 41(3),
540-544.
Gustafsson, V. (2006). Entrepreneurial decision-making: Individuals, tasks and cognitions.
Edward Elgar Publishing.
Hassan, I. H. (2001). From postmodernism to postmodernity: The local/global context.
Philosophy and literature, 25(1), 1-13.
Hatton, E. (1989). Levi-Strauss "Bricolage" and Theorizing Teachers' Work. Anthropology &
Education Quarterly, 74-96.
Hayton, J. C., George, G., & Zahra, S. A. (2002). National culture and entrepreneurship: A
review of behavioral research. Entrepreneurship theory and practice, 26(4), 33.
Hébert, R. F., & Link, A. N. (2009). A history of entrepreneurship. Routledge.
Helmke, G., & Levitsky, S. (2004). Informal institutions and comparative politics: A research
agenda. Perspectives on politics, 2(4), 725-740.
Hofstede, G. (1980). Culture and organizations. International Studies of Management &
Organization, 10(4), 15-41.
Howell, D. C. (2012). Statistical methods for psychology. Cengage Learning.
Joerges, B. (1999). Do politics have artefacts?. Social studies of science, 29(3), 411-431.
Kaplan, R. S., & Norton, D. P. (2001). The strategy-focused organization: How balanced
scorecard companies thrive in the new business environment. Harvard Business Press.
Kothari, C. R. (2004). Research methodology: Methods and techniques. New Age
International.
Kolesnik Tatyana, N., & Demidova Aleksandra, S. (2013). History of Entrepreneurship.
Business Inform, (1)
Kuhn, T. S., & Hawkins, D. (1963). The structure of scientific revolutions. American Journal
of Physics, 31(7), 554-555.
Langlois, R. N., & Cosgel, M. M. (1993). Frank Knight on risk, uncertainty, and the firm: a
new interpretation. Economic inquiry, 31(3), 456-465.
Laskovaia, A., Shirokova, G., & Morris, M. H. (2017). National culture, effectuation, and
new venture performance: global evidence from student entrepreneurs. Small Business
Economics, 1-23
Levi-Strauss, C. (1966). The savage mind. University of Chicago Press
Lewens, T. (2013). From bricolage to BioBricks™: Synthetic biology and rational design. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of
Biological and Biomedical Sciences, 44(4), 641-648.
Low, M. B., & MacMillan, I. C. (1988). Entrepreneurship: Past research and future
challenges. Journal of management, 14(2), 139-161.
Li, Y., & Zahra, S. A. (2012). Formal institutions, culture, and venture capital activity: A
cross-country analysis. Journal of Business Venturing, 27(1), 95-111.
Meek, W. R., Pacheco, D. F., & York, J. G. (2010). The impact of social norms on
entrepreneurial action: Evidence from the environmental entrepreneurship context. Journal of
Business Venturing, 25(5), 493-509.
Mitchell, R. K., Smith, J. B., Morse, E. A., Seawright, K. W., Peredo, A. M., & McKenzie, B.
(2002). Are entrepreneurial cognitions universal? Assessing entrepreneurial cognitions across
cultures. Entrepreneurship: Theory and Practice, 26(4), 9-33.
Mitchell, R. K., Smith, B., Seawright, K. W., & Morse, E. A. (2000). Cross-cultural
cognitions and the venture creation decision. Academy of management Journal, 43(5), 974-
993.
Mitchell, J. B. (1989). Current theories on expert and novice thinking: A full faculty
considers the implications for legal education. Journal of Legal Education, 39(2), 275-297.
Mitchell, J. R., Smith, J. B., Gustafsson, V., Davidsson, P., & Mitchell, R. K. (2005).
Thinking about thinking about thinking: Exploring how entrepreneurial metacognition affects
entrepreneurial expertise. In presented June (Vol. 10, p. 2005).
Montiel, Ivan & Delgado-Ceballos, Javier. (2014). Defining and Measuring Corporate
Sustainability: Are We There Yet? Organization & Environment. 27. 113-139.
Mrazek, A. J., Chiao, J. Y., Blizinsky, K. D., Lun, J., & Gelfand, M. J. (2013). The role of
culture–gene coevolution in morality judgment: Examining the interplay between tightness–looseness and allelic variation of the serotonin transporter gene. Culture and Brain, 1(2-4),
100-117.
Murphy, G. B., Trailer, J. W., & Hill, R. C. (1996). Measuring performance in
entrepreneurship research. Journal of business research, 36(1), 15-23.
Novak, T. P., & Hoffman, D. L. (2008). The fit of thinking style and situation: New measures
of situation-specific experiential and rational cognition. Journal of Consumer Research,
36(1), 56-72
Peng, M. W. (2003). Institutional transitions and strategic choices. Academy of management
review, 28(2), 275-296.
Perkmann, M., & Spicer, A. (2014). How emerging organizations take form: The role of
imprinting and values in organizational bricolage. Organization Science, 25(6), 1785-1806.
Perry, J. T., Chandler, G. N., & Markova, G. (2012). Entrepreneurial effectuation: a review
and suggestions for future research. Entrepreneurship Theory and Practice, 36(4), 837-861.
Phillips, N., & Tracey, P. (2007). Opportunity recognition, entrepreneurial capabilities and
bricolage: connecting institutional theory and entrepreneurship in strategic organization.
Strategic organization, 5(3), 313-320.
Praag, C. M., & Versloot, P. H. (2007). What is the value of entrepreneurship? A review of
recent research. Small business economics, 29(4), 351-382.
Read, S., & Sarasvathy, S. D. (2005). Knowing what to do and doing what you know:
Effectuation as a form of entrepreneurial expertise. The Journal of Private Equity, 9(1), 45-
62.
Read, S., Song, M., & Smit, W. (2009). A meta-analytic review of effectuation and venture
performance. Journal of Business Venturing, 24(6), 573-587.
Read, S., Sarasvathy, S. D., Dew, N., & Wiltbank, R. (2016). Response to Arend, Sarooghi,
and Burkemper (2015): Cocreating effectual entrepreneurship research. Academy of
management Review, 41(3), 528-536.
Read, S., & Sarasvathy, S. Knowing what to do and doing what you know: Effectuation as a
form of entrepreneurial expertise
Roure, J. B., & Maidique, M. A. (1986). Linking prefunding factors and high-technology
venture success: An exploratory study. Journal of Business Venturing, 1(3), 295-306.
Sand, M. (2017). The virtues and vices of innovators. Philosophy of Management, 1-17.
Sarasvathy, S. D. (2008). Effectuation: Elements of entrepreneurial expertise. Edward Elgar
Publishing
Sarasvathy, S. D. (2001). Causation and effectuation: Toward a theoretical shift from
economic inevitability to entrepreneurial contingency. Academy of management Review,
26(2), 243-263.
Sarasvathy, S. D., Dew, N., Read, S., & Wiltbank, R. (2008). Designing organizations that
design environments: Lessons from entrepreneurial expertise. Organization Studies, 29(3),
331-350.
Sarasvathy, S., Kumar, K., York, J. G., & Bhagavatula, S. (2014). An effectual approach to
international entrepreneurship: Overlaps, challenges, and provocative possibilities.
Entrepreneurship Theory and Practice, 38(1), 71-93.
Sarasvathy, S. D., & Dew, N. (2005). Entrepreneurial logics for a technology of foolishness.
Scandinavian Journal of Management, 21(4), 385-406.
Sarasvathy, S. D., Menon, A. R., & Kuechle, G. (2013). Failing firms and successful
entrepreneurs: Serial entrepreneurship as a temporal portfolio. Small business economics,
40(2), 417-434.
Shane, S., & Venkataraman, S. (2000). The promise of entrepreneurship as a field of
research. Academy of management review, 25(1), 217-226
Sarasvathy, S. D., Dew, N., Velamuri, S. R., & Venkataraman, S. (2003). Three views of
entrepreneurial opportunity. In Handbook of entrepreneurship research (pp. 141-160).
Springer US.
Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach's alpha. International journal
of medical education, 2, 53.
Trochim, W. (2006). Social Research Methods. Retrieved 11-04-2018 from
https://socialresearchmethods.net/
Triandis, H. C. (2004). The many dimensions of culture. The Academy of Management
Executive, 18(1), 88-93.
Tung, R. L., & Verbeke, A. (2010). Beyond Hofstede and GLOBE: Improving the quality of
cross-cultural research.
Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and
probability. Cognitive psychology, 5(2), 207-232
Uz, I. (2015). The index of cultural tightness and looseness among 68 countries. Journal of
Cross-Cultural Psychology, 46(3), 319-335.
Venkatraman, N., & Ramanujam, V. (1986). Measurement of business performance in
strategy research: A comparison of approaches. Academy of management review, 11(4), 801-
814.
Venkataraman, S. (1997). The distinctive domain of entrepreneurship research. Advances in
entrepreneurship, firm emergence and growth, 3(1), 119-138.
Visscher, K., Heusinkveld, S., O’Mahoney, J. (2017) Bricolage and Identity Work. British
Journal of Management, 00, pp.1-17. DOI: 10.1111/1467-8551.12220.
Wagner, J. (1990). "Bricolage" and Teachers' Theorizing. Anthropology & Education
Quarterly, 78-81.
Weber, E. U., & Morris, M. W. (2010). Culture and judgment and decision making: The
constructivist turn. Perspectives on Psychological Science, 5(4), 410-419.
Welter, C., Mauer, R., & Wuebker, R. J. (2016). Bridging behavioral models and theoretical
concepts: effectuation and bricolage in the opportunity creation framework. Strategic
Entrepreneurship Journal, 10(1), 5-20.
Williamson, J. G. (1996). Globalization, convergence, and history. The Journal of Economic
History, 56(2), 277-306.
Wiltbank, R., Read, S., Dew, N., & Sarasvathy, S. D. (2009). Prediction and control under
uncertainty: Outcomes in angel investing. Journal of Business Venturing, 24(2), 116-133.
Wiltbank, R., Dew, N., Read, S., & Sarasvathy, S. D. (2006). What to do next? The case for
non‐predictive strategy. Strategic management journal, 27(10), 981-998.
Wong, P. K., Ho, Y. P., & Autio, E. (2005). Entrepreneurship, innovation and economic
growth: Evidence from GEM data. Small business economics, 24(3), 335-350.
York, J. G., & Venkataraman, S. (2010). The entrepreneur–environment nexus: Uncertainty,
innovation, and allocation. Journal of Business Venturing, 25(5), 449-463.
Zahra, S. A. (2007). Contextualizing theory building in entrepreneurship research. Journal of
Business venturing, 22(3), 443-452.
Zhao, H., & Seibert, S. E. (2006). The big five personality dimensions and entrepreneurial
status: a meta-analytical review.
Appendix A. Summary of Hierarchical Regression Analysis for Variables Predicting Causal
Decision Making (N = 272)
Model 1 Model 2 Model 3 Model 4 (Z values)
B SEB t p B SEB t p B SEB t p B SEB t p
Age 0,00 0,00 10,01 0,00 0,00 0,00 -1,59 0,11 0,00 0,00 -1,05 0,29 0,00 0,00 -1,05 0,29
Gender -0,04 0,02 2,52 0,01 -0,03 0,02 -1,36 0,17 -0,03 0,02 -1,36 0,17 0.26 0.21 1.24 0.22
Education Bachelor 0,04 0,04 -1,80 0,07 -0,03 0,03 -0,73 0,46 -0,02 0,04 -0,54 0,59 0.22 0.17 1.31 0.19
Education Master 0,04 0,03 0,91 0,36 -0,01 0,03 -0,54 0,59 0,01 0,03 0,33 0,74 0.19 0.22 0.86 0.39
Education PhD -0,18 0,13 1,43 0,16 -0,15 0,11 -1,34 0,18 -0,10 0,11 -0,88 0,38 0.05 0.15 0.36 0.72
Need for Cognition 0,45 0,06 7,55 0,00 0,40 0,06 6,47 0,00 -0.03 0.03 -0.77 0.44
Faith in Intuition
0,13 0,07 1,90 0,06
0,05 0,07 0,62 0,53
0,00 0,01 -0,21 0,83
Cultural Tightness
0,26 0,08 3,11 0,00
0.26 0.21 1.38 0.22
Expertise
0,00 0,00 -1,55 0,12
0.22 0.17 1.45 0.19
Culture_Intuition
0.19 0.22 0.14 0.39
Culture_Cognition
0.05 0.15 0.64 0.72
Expertise_Intuition
-0.04 0.03 -0.78 0.44
Expertise_Cognition
-0,03 0,01 -2,27 0,02
Adj. R2 F Df p Adj. R2 F Df p Adj. R2 F Df p Adj. R2 F Df p
0,05 2,89 5;259 0,01 0,30 49,00 2;257 0,00 0,32 6,22 2;255 0,00 0,34 2,68 4;251 0,03
Appendix B. Factor analysis Thinking Style